US20080243970A1 - Method and system for providing loitering trace in virtual machines - Google Patents

Method and system for providing loitering trace in virtual machines Download PDF

Info

Publication number
US20080243970A1
US20080243970A1 US11/731,499 US73149907A US2008243970A1 US 20080243970 A1 US20080243970 A1 US 20080243970A1 US 73149907 A US73149907 A US 73149907A US 2008243970 A1 US2008243970 A1 US 2008243970A1
Authority
US
United States
Prior art keywords
objects
profiling
trace
java
server
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Abandoned
Application number
US11/731,499
Inventor
Ralf Schmelter
Michael Wintergerst
Arno Zeller
Oliver Bendig
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
SAP SE
Original Assignee
SAP SE
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by SAP SE filed Critical SAP SE
Priority to US11/731,499 priority Critical patent/US20080243970A1/en
Assigned to SAP AG reassignment SAP AG ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: SCHMELTER, RALF, BENDIG, OLIVER, WINTERGERST, MICHAEL, ZELLER, ARNO
Publication of US20080243970A1 publication Critical patent/US20080243970A1/en
Priority to US12/638,500 priority patent/US7971010B2/en
Abandoned legal-status Critical Current

Links

Images

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F12/00Accessing, addressing or allocating within memory systems or architectures
    • G06F12/02Addressing or allocation; Relocation
    • G06F12/0223User address space allocation, e.g. contiguous or non contiguous base addressing
    • G06F12/023Free address space management
    • G06F12/0253Garbage collection, i.e. reclamation of unreferenced memory
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00Error detection; Error correction; Monitoring
    • G06F11/30Monitoring
    • G06F11/34Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
    • G06F11/3466Performance evaluation by tracing or monitoring
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F2201/00Indexing scheme relating to error detection, to error correction, and to monitoring
    • G06F2201/865Monitoring of software

Definitions

  • Embodiments of the invention relate generally to the field of data processing systems. More particularly, the embodiments of the invention relate to provide loitering trace in virtual machines.
  • a memory on any computing system is a limited resource. No matter how fast computing systems become, they always depend upon a finite amount of memory in which to run their software applications. As a result, software developers should consider this resource when writing and developing software applications.
  • the Java programming language differs from many traditional programming languages (e.g., C, C++) by the way in which memory is allocated and deallocated.
  • memory is explicitly allocated and deallocated by the application programmer/developer. This can greatly increase the time spent by programmers in tracking down coding defects in regards to deallocating memory.
  • the Java programming language presents several features that appeal to developers of large-scale distributed systems, such as “write once, run anywhere” portability, portable support for multithreaded programming, support for distributed programming, including remote method invocation, garbage collection, and an appealing object model have encouraged Java use for systems with a size and complexity far beyond small applets.
  • the developers of these applications often encounter problems, such as memory leaks, performance and scalability problems, synchronization problems, and programming errors.
  • Java runtime environments provide a built-in mechanism for allocating and deallocating memory.
  • memory is allocated to objects.
  • the Java virtual machine (“VM” or “JVM”) automatically handles the amount and allocation of memory upon an object's creation.
  • the Java runtime environment employs a “garbage collector” (GC) to reclaim the memory allocated to an object that is no longer needed. Once the GC determines that the object is no longer accessible (e.g., when there is no longer any references to it stored in any variables, the fields of objects, or the elements of any arrays, etc.), it reclaims the allocated memory.
  • the heap space the object occupied is to be recycled so that the space becomes available for subsequently-created objects.
  • a memory leak can occur when a program (or in the case of Java, the VM) allocates memory to an object but never (or only partially) deallocates the memory when the object is no longer needed. As a result, a continually increasing block of memory may be allocated to the object, eventually resulting in an “Out Of Memory Error” (OOME). In other words, a memory leak occurs when memory is allocated, but it is never (or only partially) reclaimed.
  • OOME Out Of Memory Error
  • Memory leaks can also occur when a data structure (e.g., hashtable) is used to associated one object with another and even when neither object is required any longer, the association with the data structure remains, preventing the objects from being reclaims until the data structure is reclaimed. Stated differently, when a lifetime of the data structure is longer than that of the objects associated with it, memory leaks are caused.
  • a data structure e.g., hashtable
  • Memory leaks are of particular concern on Java-based systems (e.g., Java 2 Platform Enterprise Edition (J2EE) platforms) which are to run twenty-four hours a day, seven days a week. In this case, memory leaks, even seemingly insignificant ones, can become a major problem. Even the smallest memory leak in code that runs 24/7 may eventually cause an OOME, which can bring down the VM and its applications or even all VMs running on a particular application server instance. This can cause critical performance problems.
  • Java-based systems e.g., Java 2 Platform Enterprise Edition (J2EE) platforms
  • J2EE Java 2 Platform Enterprise Edition
  • garbage collection makes code much safer, because having the developer to explicitly delete objects from memory is prone to human error, garbage collection is not a panacea. For example, if the developer does not manage the references to the Java objects carefully, it can result in a memory leak problem, such as a reference to an object is stored within an instance or class field, this reference may exist throughout the life of the application and, unless desired, is regarded a memory leak.
  • ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇
  • Memory leaks can occur for various reasons, such as due to a clean-up operation failure at a cache that can cause memory leaks.
  • No conventional methods or systems provide for detection of entries or object resulting from the operation failure that cause such memory leaks or the time period during which these entries or objects are used.
  • FIG. 1 illustrates a conventional profiling tool.
  • Client 102 is in communication with server 108 .
  • Client 102 includes a VM 102 .
  • Server 108 includes a VM 112 , which includes Java Virtual Machine Profiling Interface (JVMPI)-based interface 116 and implementation 114 .
  • Server 108 further includes a native/default profiling agent (having an agent library) 110 which is plugged into the VM 112 at start-up. Since JVMPI is a native/default-interface, the agent 110 is also written in native code.
  • An agent 110 refers to a software entity, which is used to gather profiling information native VM interfaces (e.g., JVMPI).
  • JVMPI-based implementation 114 suffers from high memory footprints and, like conventional tools JProbe and Wily Introscope, requires a VM restart.
  • conventional profiling tools e.g., also those using Java Virtual Machine Tool Interface (JVMTI)
  • JVMTI Java Virtual Machine Tool Interface
  • the VM 112 is to be restarted in special way, such as by having the agent 110 loaded at VM-startup, which can cause negative impact on performance and memory consumption.
  • monitoring tools and debugging tools e.g., using Java Virtual Machine Debugging Interface (JVMDI) also suffer from these and additional limitations.
  • a system and method are provided for performing loitering trace in virtual machines.
  • status of objects in a garbage collection heap at a first virtual machine at a server is identified, in response to a memory leak.
  • First objects that are used are identified.
  • Second objects that are alive and not being used are identified.
  • Information regarding the first objects and the second objects is communicated to a second virtual machine at a client.
  • FIG. 1 illustrates a conventional profiling tool.
  • FIG. 2 illustrates an embodiment of a server having an embodiment of an on-demand profiling infrastructure.
  • FIG. 3 illustrates an embodiment of a backend VM having an embodiment of an on-demand profiling infrastructure.
  • FIG. 4 illustrates an embodiment of a process for profiling using an embodiment of an on-demand profiling infrastructure.
  • FIG. 5 illustrates an embodiment of a mechanism for performing loitering trace at a virtual machine.
  • FIG. 6 illustrates an embodiment of a transaction sequence for performing loitering trace.
  • FIG. 7 illustrates an embodiment of a process for performing loitering trace.
  • FIG. 8 illustrates an embodiment of a monitoring tool.
  • FIG. 9 illustrates an embodiment of a computing system.
  • FIG. 10 illustrates an embodiment of a client/server network system employing a message enhancement mechanism.
  • references to one or more “embodiments” are understood as describing a particular feature, structure, or characteristic included in at least one implementation of the invention.
  • phrases such as “in one embodiment” or “in an alternate embodiment” appearing herein describe various embodiments and implementations of the invention, and do not necessarily all refer to the same embodiment. However, they are also not necessarily mutually exclusive. Descriptions of certain details and implementations follow, including a description of the figures, which may depict some or all of the embodiments described below, as well as discussing other potential embodiments or implementations of the inventive concepts presented herein.
  • Java applications can vary in both size and complexity.
  • certain large Java application e.g., ⁇ 10,000 classes and ⁇ 1,000,000 methods with ⁇ 100,000,000 method calls
  • major problems e.g., memory leaks
  • major problems e.g., memory leaks
  • a single long living object that increases in size by 1 byte between each GC cycle will eventually cause the application and VM to crash due to an OOME. Although such a crash may take a long time (e.g., 1 bytes per GC cycle*millions of free bytes of memory), it will inevitably occur.
  • a vendor-specific proprietary interface and implementation are provided, as described throughout this document (e.g., see FIG. 1 ).
  • This implementation can be made an integral part of a VM (e.g., JVM, SAP JVM) and allow for on-demand examining of system problems, including in productive systems, without restarting the underlying VM.
  • system problems can range anywhere from memory leaks to performance, scalability and synchronization problems.
  • “on-demand” refers to examining (e.g., profiling, tracing, debugging, and/or monitoring) system problems in runtime, such as without the need for restarting the underlying VM.
  • FIG. 2 illustrates an embodiment of a server 202 having an embodiment of an on-demand profiling infrastructure 208 .
  • Sever 202 comprises a backend VM 206 (e.g., JVM, SAP JVM) having an embodiment of an on-demand profiling framework or infrastructure (profiling infrastructure) 208 .
  • Profiling infrastructure 208 is shown in communication with a server Java application programming interface (API) 204 .
  • API server Java application programming interface
  • profiling infrastructure 208 is implemented as an intrinsic and direct part of the underlying VM 206 and is embedded within the backend VM 206 , rather than relying on native profiling interfaces, such as JVMTI and JVMPI, and agent, for implementation.
  • backend VM 206 e.g., Java VM
  • server 202 e.g., J2EE server
  • profiling is performed using profiling infrastructure 208 that resides at backend VM 206 that is being profiled.
  • Profiling infrastructure 208 includes a number of components (as described in FIG. 3 ) to perform trace profiling.
  • no default profiling agent or default implementations and instances e.g., JVMPI, JVMTI
  • a direct communication is established between backend VM 206 and frontend VM 214 via server Java API 204 and client Java API 212 and profiling protocol 210 . Any number of VMs may be used as backend or frontend VMs.
  • an external profiling file 218 is used to store profiling trace data.
  • Starting and stopping of profiling trace may be performed in a number of ways, such as using a Graphical User Interface (GUI)-based monitoring tool 220 .
  • GUI Graphical User Interface
  • the profiling data is written using various components of profiling infrastructure 208 and displayed to the user using any number of display devices. These display devices may include GUI-based display devices.
  • on-demand profiling is performed which refers to performing the profiling without restarting the underlying VM 206 . Stated differently, the profiling is performed in runtime without any interruptions or restarting of the underlying VM 206 .
  • Profiling infrastructure 208 can be used for starting profiling traces for certain users or applications, such as using profiling annotations.
  • Profiling annotations refer to a concept of tagging threads with certain semantic information from an application server environment.
  • Java API 204 is provided which allows for annotating a Java thread with one or more of the following information: user name, application name, request identifier, and session identifier. If profiling traces are started, a thread filter for such information is provided and thus, a profiling trace can be started only a certain user or application.
  • a Java API is also provided on the client-side, such as client Java API 212 , that communication with server Java API 204 via a profiling protocol 210 .
  • Client 216 includes frontend VM 214 , which includes any arbitrary VM that represents a native application that speaks (e.g., in case of online profiling) the profiling protocol 210 and/or knows (e.g., in case of offline profiling) the profiling file format of profiling file 218 .
  • Backend VM 206 is the one that is being profiled.
  • the VMs 206 , 214 may not be VMs and instead be any program or application (e.g., a native application or program) that is compatible with the components of and related to the profiling infrastructure 208 .
  • the frontend VM 214 is illustrated here merely as an example for brevity and clarity. It is, however, contemplated that a frontend VM 214 or any VM for that matter is not necessary for embodiments of the present invention.
  • any program or application that is compatible with the mechanisms and components described herein is acceptable and functional and can be employed and implemented.
  • any program that can read and speak the described components e.g., components of profiling infrastructure 208 ), protocols (e.g., socket communication protocol), APIs (e.g., server- and client-side APIs 204 , 212 ), parameters, profiling files 218 , etc.
  • a VM such as the frontend VM 214 .
  • the illustrated mechanism 200 provides both an online mechanism for (interactive) profiling and an offline mechanism for (non-interactive) profiling.
  • any profiling parameters including the desired mode, e.g., an online or offline mode, are specified.
  • the profiling backend VM 206 opens a port and waits for a connection.
  • the profiling frontend VM 214 attach to this connection via the profiling protocol 210 and Java APIs 204 , 212 . The starting, running, and stopping of profiling and tracing is then performed.
  • online profiling is performed via internal components, such as Java APIs 204 , 212 , or external components, such as a monitoring tool (e.g., Java VM monitor) 220 .
  • profiling files 218 are used to store profiling data and a special interface is provided to couple the backend VM 206 with the frontend VM 214 via client Java API 212 to allow for starting and stopping of traces.
  • server Java API 204 can also be used to perform offline profiling. Offline profiling may also be performed using monitoring tool 220 and/or using a command line, such as java ⁇ XX: +Profiling ⁇ XX:+ProfilingAlloationTrace.
  • the profiling information is stored in an external medium 218 (e.g., file system) and can be analyzed after the profiling run. This way, the profiling information may then be used for port-mortem analysis; however, traces can still be started and stopped in an interactive manner.
  • the online or interactive mode allows for analyzing the profiling information online. For example, if a class statistic trace has been enabled and a garbage collection happens, the profiling information can be directly accessible through a stream-based interface.
  • VM 206 may maintain a global flag indicating whether profiling is enabled or not. The flag may be requested each time any profiling data is written. For example, a profiling trace for garbage collection events may be implemented in the following way: when a garbage collection is performed, the global profiling flag is checked. If profiling is enabled, the flag is checked to indicate whether garbage collection events are to be profiled. This can also be done via some VM global flags. If the garbage collection trace is enabled, the backend VM 206 may be called to collect the desired data.
  • FIG. 3 illustrates an embodiment of a backend VM 206 having an embodiment of an on-demand profiling infrastructure 208 .
  • profiling infrastructure 208 contains controller framework 302 , thread filter 304 , buffer framework 306 , class filter 308 , identification service 310 , communication framework 312 , object identification service 314 , allocation trace module 316 , loitering trace module 318 , garbage collection trace module 320 , and other trace modules 322 to perform other traces.
  • profiling controller framework 302 is used for starting and stopping profiling runs and traces. Controller framework 302 allows the user to specify profiling options or settings that the user would want to enable. These profiling settings to be applied are divided into distinct areas, such as functional profiling settings and filter settings.
  • the functional profiling settings determine the area to be profiled (e.g., allocation trace, reference trace, etc.), while the filter settings define the validity scope (e.g., user, session, thread, VM, etc.) of the functional profiling settings. For example, an allocation trace can be started for a specified user.
  • Java API and graphical user interface are provided in communication with profiling controller framework 302 . GUI is used to enable the user to directly specify the desired profiling settings without any system-guidance.
  • Controller framework 302 may include a profiling evaluation module for analyzing a performed profiling run.
  • the Java API can be used for getting the complete low-level profiling information gathered within a corresponding profiling run as well as for getting condensed, problem-oriented profiling information.
  • the condensed profiling information may be used to directly pinpoint various problematic areas. For example, if the user has performed performance analysis using a time-based sampling approach, the Java API may enable a client to directly receive information about the time-consuming methods. The user may view this information via GUI at a display device at the client.
  • Controller framework 302 is used for starting and stopping profiling runs and traces, which includes starting and stopping various profiling options (further described later). For each profiling run the user is free to determine the set of traces to be started. For example, the user may start an allocation trace using the allocation trace module 316 together with a class statistic trace. A user-defined name may be assigned to each non-interactive profiling run and used later on to evaluate the gathered profiling information. Considering interactive profiling runs, the user is able to evaluate the profiling information online and therefore, the profiling information may be available through a stream-based interface.
  • controller framework 302 may be independent of the surrounding application server environment. Stated differently, controller framework 302 refers to the underlying VM 206 currently executing a profiling request (e.g., starting an allocation trace). The corresponding application server infrastructure may be responsible for starting and stopping the desired trace on other VMs. For example, if an allocation trace is started for a certain user session at VM 208 , the application server infrastructure accounts for starting the allocation trace in the VMs executing requests for the user session. Controller framework 302 enables the application server infrastructure to specify thread filters 304 .
  • a thread filter 304 may contain the following information: client, user, session identifier, request identifier, application name, and component name.
  • controller framework 302 may provide a facility to tag these pieces of information to a thread.
  • a thread filter 304 is provided.
  • the application server is responsible for setting the current thread state (e.g., client, user, session identifier, etc.).
  • an application server includes a J2EE server.
  • the profiling options include functions/cases, such as memory debugging (e.g., memory leak detection), performance analysis, synchronization monitoring, and application debugging (e.g., detecting called methods).
  • These profiling functions further include a number of sub-functions, such as heap dump, coupling of debugging and profiling infrastructure, time-based sampling, memory-based sampling, method statistic, allocation trace, silent allocation trace, allocation statistic trace, loitering trace, garbage collection trace, garbage collection statistic, class statistic trace, permanent generation statistic trace, local garbage collection trace, other traces, such as reference trace, object death trace, object movement trace, global reference trace, method trace, time method trace, input/output (I/O) trace, monitor trace, shared lock trace, method count trace, execution line trace, scheduler trace, and exception trace.
  • memory debugging e.g., memory leak detection
  • performance analysis e.g., synchronization monitoring
  • application debugging e.g., detecting called methods
  • These profiling functions further include a number of sub
  • Solving a memory leak problem may include a couple of processes, such as identifying the Java classes or objects caused the memory leak, and determining where in the infrastructure or application code the leak occurred. Many of the sub functions can be used to solve memory leak problems.
  • Class statistic trace functionality is provided to help identify the Java classes that cause memory leaks.
  • Class statistic trace includes getting an overview of all living classes within particular VM, including class name, class loader description, the number of object instances, and the accumulated net and gross size of all object instances. The information may be traced after each full local garbage collection.
  • Reference trace includes detecting the objects holding references to leaking objects. It also provides the complete reference chain to a specific object instance. This information may also be available after one full local garbage collection.
  • the allocation trace may be enabled to check for the exact allocation place.
  • the allocation trace enables the user to specify a class filter 308 .
  • Silent allocation trace is a derivate of allocation trace. When an allocation trace is started, each object, which is allocated and adheres to a user-defined class filter 308 , is assigned to an object identifier. Although the allocation trace enables the user to get informed about object allocations; the user may not get the information when the corresponding object dies. In that case, object death trace allows the user to check for those objects are garbage collected and no longer alive. Object movement trace makes allows the checking of why certain objects are kept alive, while the allocation trace allows for getting information when certain objects are created.
  • memory leaks occur due to the fact that a failed clean-up operation. For example, considering a, at regular intervals, the cache might be cleared. If the clean-up operation were interrupted at the end of the operation (e.g., due to a VM abort exception), most cache entries would probably be deleted; however, some entries might still exist. Thus, a memory leak may be resulted if the cache were not able to remove any of the existing entries. The detection of this kind of memory leak could be difficult, since most object instances of the corresponding class are removed and merely a few exist. Thus, class statistic trace may not be the right choice to detect such a memory leak.
  • One characteristic of this problem is that the memory leak is caused by objects which may not be used any longer.
  • the loitering trace performed via loitering trace module 318 facilitates the detection of objects which are not used for a long time.
  • Profiling helps improving the performance by determining what is it that is to be optimized.
  • Profiling identifies parts of the overall system for which optimization can have an impact on the overall performance. Optimizing a function which only amounts to a miniscule fraction of the overall runtime may not have noticeable benefits.
  • Profiling also determines how the optimization is to be done. Checking for optimization options of those parts that are identified during the first process. Time-based sampling is used to get an overview of methods, which consume the most CPU resources of the application.
  • Time-based sampling works by dumping a stack trace of the currently active thread at regular intervals.
  • Memory-based sampling works analogously to the time-base sampling; however instead of dumping a stack trace in time intervals ( ⁇ t), stack trace is sampled after an amount of memory ( ⁇ M) is allocated on the Java heap. This way, those methods that allocate the largest number of bytes on the Java heap are identified.
  • method statistic trace may be used. Together with time-based sampling, method statistic trace may also allow for calculating the average runtime of a specific method (e.g., the “cumulative time” divided by the method count). Method trace is used to get more detailed information than method statistic. Time method trace can be used to provide very detailed trace information. Time method trace provides for detecting method calls that (for any number of reasons) take a particularly long time. To see, if garbage collection is properly configured or if a particular problem related to garbage collection exists, local GC statistic is used, which includes dumping a statistical entry for each local garbage collection (partial and full) for each garbage collection run.
  • I/O trace allows for tracing the timing of each I/O operation. I/O trace can be used in analysis to check for operations, where huge amounts of data were transmitted, the I/O operation took an extraordinary amount of time, or a huge amount of small I/O operations was performed.
  • synchronization monitoring includes monitor trace that identifies deadlock or scalability problems and gathers information about locks used inside a VM. To find synchronization problems, a thread trying to acquire a lock is identified and once it is identified, the lock is freed by the thread. Shared lock trace is used to identify deadlocks between VMs and scalability problems of a server instance. Shared lock trace provides information about different kinds of shared lock activities, like entering and leaving. Further, for such problems above, scheduler trace is used to know why a thread was scheduled and why it gave up control of the CPU, and for how long the entire VM was waiting on external I/O or just sleeping.
  • application debugging is used to provide those the debugging functionalities that are not supported by conventional debugging instances and protocols, such as JVMDI, Java Debug Wire Protocol (JDWP), etc.
  • application debugging covers functionalities, such as call coverage and line coverage.
  • call coverage method count trace may deliver a number of calls to a method.
  • execution line trace may deliver information about code lines that were executed.
  • Method call trace is used to find all methods that are called.
  • the VM 206 counts method calls and when the method call trace is disabled, the VM 206 dumps the collected information, such as name and signature of a method and the number of times it was called.
  • Execution line trace may be used to find out the lines of code that are not executed. When the execution line trace is triggered, it enables the VM to write out information about the method and code line each time a byte code is interpreted and/or the line number changes. Such information can help the developer find out the lines of code that are not covered particular test cases.
  • Method trace may be employed to trace or profile the debugging process of an application. For example, the method trace is used to find out what has happened before the program reaches a certain point. Such information may be used to trace back the program flow and find out in which way the program reached that point of code.
  • Exception trace is another functionality that may be employed to trace or profile the debugging process of an application. This information can be used to trace back the reasons for exceptions that followed up and for different execution branches.
  • a dedicated Java API and a GUI is provided to allow for starting and stopping of various functionalities and uses (e.g., allocation trace, loitering trace, GC trace, and other traces) and for getting the corresponding profiling and tracing results.
  • an expert mode and/or a guided mode are provided.
  • a guided mode may directly pinpoint any problem areas.
  • Profiling infrastructure 208 is compatible with multiple clients. For example, depending on the surrounding application server infrastructure and whether any clients are handled in a special way, the profiling infrastructure 208 may perform in compliance with several clients, simultaneously, and remain multiple client-compliant. profiling infrastructure 208 also allows for restricting profiling runs to certain clients, while the surrounding application server environment may assure that the current client information is assigned to the respective thread. Furthermore, profiling infrastructure 208 may be started on-demand, which includes performing profiling infrastructure functionalities (e.g., profiling, tracing, etc.) without restarting the entire application server or even the underlying VM 206 . If no profiling option is enabled by a certain user, there is no impact on the response time caused by the profiling infrastructure 208 .
  • profiling infrastructure functionalities e.g., profiling, tracing, etc.
  • profiling may depend on the started profiling options and filter settings about how the overall system performance is influenced. For example, if a method trace is started on an application server without any filter settings (e.g., user, classes, etc.), the performance may decrease to an extent. Therefore, the profiling infrastructure 208 as well as the application server infrastructure must provide options to restrict profiling runs. This way, profiling may be enabled for a particular user or session, while users and sessions remain unaffected. In addition, profiling infrastructure 208 provides reasonable and necessary filter settings for various profiling traces.
  • filter settings e.g., user, classes, etc.
  • Class filters 308 are implemented to allow for limiting profiling trace outputs by limiting the process of profiling to, for example, specific traces. For example, if a developer seeks to profile only Java object allocations which refer to java.lang.HashMap instances, then, using class filters 308 , a profiling allocation trace with a class filter applying exclusively to java.lang.HashMap instances is started. Thread filters 304 relate to profiling annotations (e.g., specifying annotations), such as when an allocation trace exists. Thread filters 304 may also be used by the user to specify when and/or where a trace is to be triggered and/or used. Buffer framework 306 is used to compress and decompress any type of data or information that is being communicated, stored, etc.
  • profiling annotations e.g., specifying annotations
  • Communication framework 312 is used to facilitate communication of any data or information between and within various components, elements, modules, systems, servers, VM, etc. Communication framework 312 is also used to determine and facilitate the storing of data or information, such as storing the data using files or socket connections.
  • ID service 310 is employed to specify variables, such a class, a name of the class, etc. to assign identification to them. Once class, class names, etc. are assigned an ID (e.g., a number), they are then mapped with each other and with various components and variables via a mapping packet, instead of mapping by names. Using ID service 310 , the same can be done with threads and methods. For example, by assigning IDs (instead of names) to threads and methods, when dumping is performed, the IDs of threads and methods are dumped rather than their names. This technique of using IDs (e.g., numbers) instead of using the names is efficient, fast, and saves memory.
  • IDs e.g., numbers
  • ID numbers are mapped to various packet names, such as java.Hashtable is mapped to “2000”, the thread (named, “main”) is assigned “3”, and the user (named, “Hansi”) is assigned “7”.
  • Stack trace is then commenced using command lines, such as com.sap.test (line 30 ), com.sap.submethod (line 2003 ), etc. The even information may then be provided as 2000, 3, etc. It is known that ID number 2000 was mapped to the underlying hashtable, while ID number 3 was mapped to the thread. Using these ID's, names (e.g., main, Hansi, etc.) are not needed and instead, IDs are used, which provides an easier technique for packet name mapping.
  • object ID service 314 is used to assign IDs (e.g., numbers) to objects so the IDs can be used to, for example, identify and compare the objects, instead of using object names.
  • profiling information and any other relevant data is displayed at a display device via GUI at a client so that a user can access and evaluate the displayed information.
  • the information may also be stored at a database and/or file system for subsequent retrieval and analysis.
  • Java components such as J2EE server, Java VM, Java heap, and Java memory errors, etc., are discussed here for simplicity and brevity, it should be noted, however, that the underlying principles and embodiments of the present invention may be implemented within any type of object-oriented and runtime environments. Moreover, it should be noted that requirements and examples used in this document do not necessarily reflect the real values that a system or program would actually produce.
  • garbage collection may be invoked multiple times while checking the VM heap memory size, so that there are different VM implementations and, according to a relevant VM specification, a given VM implementation might not clean up the memory immediately after it has been requested to do so. Thus, to be sure that a memory cleanup is provoked, the memory size may be checked and the garbage collection may be invoked again, as necessary.
  • Garbage collection as described here includes a process designed to identify and reclaim blocks of memory that are dispensed by a memory allocator but are no longer “alive” or “live” (e.g., no longer being used, as determined, for example, by not being reachable from any currently referenced objects or entities).
  • Garbage collection can sometimes be handled as a background task by runtime systems rather than as an explicit task by user programs. Garbage collection can also be handled as an inlined task. Garbage collection can be used to reclaim memory in runtime systems, and there are some well-known garbage collection algorithms (e.g., reference counting, mark-sweep, mark-compact, and copying algorithms).
  • a VM (e.g., VM 206 ) is an example of a runtime system.
  • a VM refers to an abstract machine that includes an instruction set, a set of registers, a stack, a heap, and a method area, such as a machine or processor.
  • a VM essentially acts as an interface between program code and the actual processor or hardware platform on which the program code is to be executed.
  • the program code includes instructions from the VM instruction set that manipulates the resources of the VM.
  • the VM executes instructions on the processor or hardware platform on which the VM is running, and manipulates the resources of that processor or hardware platform, so as to effect the instructions of the program code.
  • a Java source program can be compiled into program code, such as bytecode.
  • Bytecode can be executed on a VM, such as JVM, running on any processor or platform.
  • the JVM can either interpret the bytecode one instruction at a time, or the bytecode can be further compiled for the real processor or platform using a just-in-time (JIT) compiler.
  • JIT just-in-time
  • the illustrated VM 206 includes a JVM (e.g., SAP JVM), which is used as an example; however, other examples of VMs, which can be used in various embodiments, include Advanced Business Application Programming (ABAP) language VMs, Common Language Runtime (CLR) VMs, and the like.
  • SAP JVM Advanced Business Application Programming
  • CLR Common Language Runtime
  • ABAP is a programming language for developing applications for SAP systems, such as SAP R/3 system, which is a widely installed business application system developed by SAP AG of Walldorf, Germany.
  • SAP R/3 system which is a widely installed business application system developed by SAP AG of Walldorf, Germany.
  • the CLR is a managed code execution environment developed by Microsoft Corp. of Redmond, Wash.
  • the discussion in this document focuses on virtual machines, and in particular Java virtual machine 104 , but it is to be understood that the techniques described herein can also be used with other types of runtime systems.
  • a runtime system includes a code execution environment that executes instructions or code in user requests and that provides runtime services for that code.
  • Code runtime services may include functionality, such as process, thread, and memory management (e.g., laying out objects in the server memory, sharing objects, managing references to objects, and garbage collecting objects).
  • Enhanced runtime services may include functionality, such as error handling and establishing security and connectivity.
  • the illustrated server 202 includes a J2EE server/engine/node, which supports Enterprise Java Bean (“EJB”) components and EJB containers (at the business layer) and Servlets and Java Server Pages (“JSP”) (at the presentation layer).
  • EJB Enterprise Java Bean
  • JSP Java Server Pages
  • FIG. 4 illustrates an embodiment of a process for profiling using an embodiment of an on-demand profiling infrastructure.
  • decision block 402 whether profiling need be started is determined. If not, the non-profiling mode is maintained, which saves system resources. If the profiling is to be started, the profiling mechanism is switched to the profiling mode at processing block 408 .
  • decision block 410 a determination is made as to whether online or offline profiling is to be performed. If the profiling is to be performed online, the online profiling mode is triggered for, for example, interactive profiling at processing block 412 .
  • Online profiling is started via Java APIs (e.g., server and client Java APIs), using monitoring tools (e.g., Java VM monitor), and/or using command lines. If the profiling is to be performed offline, the offline profiling mode is triggered for, for example, non-interactive profiling. Offline profiling can also be started using monitoring tools, using command lines, and/or via Java APIs as described above.
  • any profiling data obtained from offline profiling is stored at an external source, such as a profiling file. The data at the profiling file may be stored in zipped format.
  • decision block 414 whether the process of profiling be continued is determined. If yes, the profiling continues with decision block 410 . If not, the profiling status is switched to the non-profiling mode at processing block 416 . This non-profiling mode is maintained at processing block 406 .
  • FIG. 5 illustrates an embodiment of a mechanism 500 for performing loitering trace at a virtual machine 206 .
  • Memory leaks can occur for a number of reasons, such as, in many cases, memory leaks can occur due to the failing of a clean-up operation at a cache. For example, a cache is to be cleared at regular intervals, and if a clean-up operation is to be interrupted even at the end of the clean-up operation, some entries or objects may still exist and not be detected. These entries or objects can cause a memory leak if the cache were not able to remove such entries. Sometimes, even a follow up clean-up operation may not clear the entries remaining from the previous operation.
  • a clean-up operation refers to a process being performed at a VM 206 to remove the old and unused applications and substitute them with new applications. The old or unused applications may not be removed due to programming errors or resource failures. It is contemplated that clean-up operation is used here only as an example for brevity and that loitering trace can be used for to cure and/or prevent any number of memory leak causes.
  • mechanism 500 includes a server 202 having a VM 206 (e.g., backend VM) that is the underlying VM and is being profiled.
  • VM 206 includes a profiling infrastructure having a loitering trace module 318 and a hook 502 .
  • VM 206 is in communication with another VM 214 (e.g., backend VM 214 ) that resides at a client 216 in communication with the server 202 .
  • loitering trace module 318 is used to collect information about unused objects at the VM 206 (e.g., objects in the old generation) and generate statistics regarding the collected information.
  • Hook 502 is employed at the VM 206 to notify of the profiling infrastructure 208 each time an object is used, which includes whether (1) a method of this object is invoked; (2) a field of this object is read to written; and (3) this object is used for synchronization. If the loitering trace is enabled, the object is marked as used. This information includes identifying (1) objects that are being used and are alive, (2) objects that are not being used but are alive; and (3) objects that are dead and are not being used and are to be removed from the garbage collection process. A report including such information is then generated using the profiling infrastructure 208 to be sent to the requesting VM 214 at client 216 . It is contemplated that objects may not be removed from the heap but rather, they are remembered and reported.
  • Java-based components such as Java application server, Java VMs 206 , 214 , Java objects, etc.
  • Java application server Java VMs 206 , 214 , Java objects, etc.
  • mechanism 500 and other embodiments of the present invention can also be used with other non-Java-based environments and components.
  • a backend VM 206 may reside at the same J2EE engine as the tree generation module or at another J2EE engine or at another server.
  • a server and a client may include servers and clients similar to server 202 and client 216 , respectively, of FIG. 2 .
  • FIG. 6 illustrates an embodiment of a transaction sequence for performing loitering trace.
  • loitering trace request is initiated 602 via a client 602 .
  • Loitering trace is initiated 604 via loitering trace module 318 at profiling infrastructure 208 .
  • profiling infrastructure 208 using hook, each time an object at a VM is used, whether the loitering trace is enabled is checked 606 . If the loitering trace is enabled, the used object is marked in the heap. If the loitering trace is not enabled, the process ends and nothing further occurs.
  • Client 602 request stopping of the loitering trace 608 .
  • FIG. 7 illustrates an embodiment of a process for performing loitering trace.
  • a memory leak is detected at processing block 702 .
  • Loitering trace is started at processing block 704 .
  • the loitering trace is now running at processing block 706 and each time an object is used, it is marked as used at processing block 708 .
  • a report of all objects is formed at processing block 712 .
  • the report is communicated to one or more clients for one or more users to access, use, and evaluate.
  • the loitering trace when the loitering trace is enabled, it marks those objects that are used while it is enabled.
  • the loitering trace is stopped, the objects that were during the time the trace was active are now marked. Using this technique, which objects were used and which objects were not used are determined and a report stating that is formed and provided. To provide these results, the loitering trace is to be running at some time for a certain amount of time.
  • FIG. 8 illustrates an embodiment of a monitoring tool 220 .
  • the illustrated monitoring tool 220 includes a JVM monitoring tool (e.g., SAP JVM monitoring tool).
  • Monitoring tool 220 includes a menu line 802 that includes various menu items, such as command, debugging, etc.
  • Monitoring tool 220 further provides a list of VMs 804 , and details about any VM 806 which includes a number of items, such as performance, VM info, debugging, trace flags, local memory, etc.
  • Monitoring tool 220 further provides shared memory configuration 808 and shared memory state 810 .
  • Monitoring tool 220 is illustrated merely as an example and the embodiment of the present invention are in no way limited to using the illustrated monitoring tool 220 .
  • Processes taught by the discussion above may be performed with program code, such as machine-executable instructions, which can cause a machine (such as a “virtual machine”, a general-purpose processor disposed on a semiconductor chip, a special-purpose processor disposed on a semiconductor chip, etc.) to perform certain functions.
  • program code such as machine-executable instructions
  • a machine such as a “virtual machine”, a general-purpose processor disposed on a semiconductor chip, a special-purpose processor disposed on a semiconductor chip, etc.
  • these functions may be performed by specific hardware components that contain hardwired logic for performing the functions, or by any combination of programmed computer components and custom hardware components.
  • modules, components, or elements described throughout this document may include hardware, software, and/or a combination thereof.
  • a module includes software
  • the software data, instructions, and/or configuration may be provided via an article of manufacture by a machine/electronic device/hardware.
  • An article of manufacture may include a machine accessible/readable medium having content to provide instructions, data, etc. The content may result in an electronic device, for example, a filer, a disk, or a disk controller as described herein, performing various operations or executions described.
  • a machine accessible medium includes any mechanism that provides (i.e., stores and/or transmits) information/content in a form accessible by a machine (e.g., computing device, electronic device, electronic system/subsystem, etc.).
  • a machine accessible medium includes recordable/non-recordable media (e.g., read only memory (ROM), random access memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, etc.), as well as electrical, optical, acoustical or other form of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.), etc.
  • the machine accessible medium may further include an electronic device having code loaded on a storage that may be executed when the electronic device is in operation.
  • delivering an electronic device with such code may be understood as providing the article of manufacture with such content described above.
  • storing code on a database or other memory location and offering the code for download over a communication medium via a propagated signal may be understood as providing the article of manufacture with such content described above.
  • the code may also be downloaded from a remote computer (e.g., a server) to a requesting computer (e.g., a client) by way of data signals embodied in a propagation medium (e.g., via a communication link (e.g., a network connection)).
  • FIG. 9 illustrates an embodiment of a computing system 900 .
  • Computing system 900 may be used for implementing one or more embodiments of the present invention and for executing program code stored by an article of manufacture. It is important to recognize that the computing system 900 represents merely of various computing system architectures that can be used for the same purposes.
  • the applicable article of manufacture may include one or more fixed components (such as hard disk drive 902 or memory 906 ) and/or various movable components, such as compact disk (CD) ROM 904 , a compact disc, a magnetic tape, and the like.
  • CD compact disk
  • ROM 904 compact disk
  • CD compact disc
  • magnetic tape and the like.
  • To execute the program code typically instructions of the program code are loaded into RAM 906 .
  • processing core 908 executes the instructions.
  • a processing core may include one or more processors and a memory controller function.
  • a virtual machine or “interpreter” may run on top of the processing core (architecturally speaking) to convert abstract code (e.g., Java bytecode) into instructions that are understandable to the specific processor(s) of processing core 908 .
  • Computing system 900 further includes network interface 910 and bus 912 to connect to other systems via a network and to have various components communicate with each other, respectively.
  • FIG. 10 illustrates an embodiment of a client/server network system 1000 employing an on-demand profiling infrastructure 1018 .
  • network 1008 links server 1010 with client systems 1002 - 1006 .
  • Server 1010 includes programming data processing system suitable for implementing apparatus, programs, and/or methods in accordance with one or more embodiments of the present invention.
  • Server 1010 includes processor 1012 and memory 1014 .
  • Server 1010 provides a core operating environment for one or more runtime systems (e.g., VM 1016 ) at memory 1014 to process user requests.
  • Memory 1014 may include a shared memory area that is accessible by multiple operating system processes executing in server 1010 .
  • VM 1016 may include an enterprise server (e.g., a J2EE-compatible server or node, Web Application Server developed by SAP AG, WebSphere Application Server developed by IBM Corp. of Armonk, N.Y., and the like).
  • the enterprise server at VM 1016 may host the on-demand profiling infrastructure 1018 .
  • Memory 1014 can be used to store an operating system, a Transmission Control Protocol/Internet Protocol (TCP/IP) stack for communicating over network 1008 , and machine executable instructions executed by processor 1012 .
  • server 1010 may include multiple processors, each of which can be used to execute machine executable instructions.
  • Client systems 1002 - 1006 may execute multiple application or application interfaces. Each instance or application or application interface may constitute a user session. Each user session may generate one or more requests to be processed by server 1010 . The requests may include instructions or code to be executed on a runtime system, such as VM 1016 , on server 1010 , such as the requests made via the on-demand profiling infrastructure 1018 and its components and modules as described throughout this document.
  • a runtime system such as VM 1016
  • server 1010 such as the requests made via the on-demand profiling infrastructure 1018 and its components and modules as described throughout this document.

Abstract

A system and method are provided for performing loitering trace in virtual machines. In one embodiment, status of objects in a garbage collection heap at a first virtual machine at a server is identified, in response to a memory leak. First objects that are used are identified. Second objects that are alive and not being used are identified. Information regarding the first objects and the second objects is communicated to a second virtual machine at a client.

Description

    FIELD
  • Embodiments of the invention relate generally to the field of data processing systems. More particularly, the embodiments of the invention relate to provide loitering trace in virtual machines.
  • BACKGROUND
  • A memory on any computing system is a limited resource. No matter how fast computing systems become, they always depend upon a finite amount of memory in which to run their software applications. As a result, software developers should consider this resource when writing and developing software applications.
  • The Java programming language differs from many traditional programming languages (e.g., C, C++) by the way in which memory is allocated and deallocated. In languages like C and C++, memory is explicitly allocated and deallocated by the application programmer/developer. This can greatly increase the time spent by programmers in tracking down coding defects in regards to deallocating memory. The Java programming language presents several features that appeal to developers of large-scale distributed systems, such as “write once, run anywhere” portability, portable support for multithreaded programming, support for distributed programming, including remote method invocation, garbage collection, and an appealing object model have encouraged Java use for systems with a size and complexity far beyond small applets. However, the developers of these applications often encounter problems, such as memory leaks, performance and scalability problems, synchronization problems, and programming errors.
  • Java runtime environments (e.g., Java virtual machine) provide a built-in mechanism for allocating and deallocating memory. In Java, memory is allocated to objects. The Java virtual machine (“VM” or “JVM”) automatically handles the amount and allocation of memory upon an object's creation. The Java runtime environment employs a “garbage collector” (GC) to reclaim the memory allocated to an object that is no longer needed. Once the GC determines that the object is no longer accessible (e.g., when there is no longer any references to it stored in any variables, the fields of objects, or the elements of any arrays, etc.), it reclaims the allocated memory. When objects in a Java application are no longer referenced, the heap space the object occupied is to be recycled so that the space becomes available for subsequently-created objects.
  • Although having garbage collection improves productivity, it is not entirely immune from a class of bugs, called “memory leaks.” A memory leak can occur when a program (or in the case of Java, the VM) allocates memory to an object but never (or only partially) deallocates the memory when the object is no longer needed. As a result, a continually increasing block of memory may be allocated to the object, eventually resulting in an “Out Of Memory Error” (OOME). In other words, a memory leak occurs when memory is allocated, but it is never (or only partially) reclaimed. Memory leaks can also occur when a data structure (e.g., hashtable) is used to associated one object with another and even when neither object is required any longer, the association with the data structure remains, preventing the objects from being reclaims until the data structure is reclaimed. Stated differently, when a lifetime of the data structure is longer than that of the objects associated with it, memory leaks are caused.
  • Memory leaks are of particular concern on Java-based systems (e.g., Java 2 Platform Enterprise Edition (J2EE) platforms) which are to run twenty-four hours a day, seven days a week. In this case, memory leaks, even seemingly insignificant ones, can become a major problem. Even the smallest memory leak in code that runs 24/7 may eventually cause an OOME, which can bring down the VM and its applications or even all VMs running on a particular application server instance. This can cause critical performance problems.
  • It is generally preferred to profile memory use and debug memory leaks in an application code in the early stages of development to provide an early detection of memory problems long before the production stage. Although garbage collection makes code much safer, because having the developer to explicitly delete objects from memory is prone to human error, garbage collection is not a panacea. For example, if the developer does not manage the references to the Java objects carefully, it can result in a memory leak problem, such as a reference to an object is stored within an instance or class field, this reference may exist throughout the life of the application and, unless desired, is regarded a memory leak.
  • Within a distributed application server environment having thousand of concurrent users, performance and scalability problems are typical. The causes of problems are various, such as synchronization problems, extensive access to shared resources (e.g., database systems), bad configuration settings, etc. To provide consistency within such a system, locks with various validity scopes (e.g., VM-local, application-server-wide, and system-wide) are used; however, deadlock situations and synchronization problems exist.
  • Several performance monitoring, profiling, and debugging tools are used to examine software applications to determine resource consumption within the Java runtime environment (JRE). For example, a profiling tool may identify the most frequently executed methods and objects created in an application. A type of software performance and debugging tool is a “tracer.” However, such tools are very limited in detecting and exposing system inefficiencies and problems (e.g., memory leaks), while consuming great amounts of system resources by requiring overhead tasks, such as starting and restarting of VMs in special modes. Further, such tools are also limited in providing necessary information about system problems and the limited information that these tools may provide is not useful for applications comprising several thousand objects. This leaves developers with often insurmountable amounts of code to manually evaluate to track down the problem objects/variables, such as the specific class, method calls, etc. For example, conventional profiling tools, like Optimizelt and JProbe, when used, require restarting of VMs and servers, which results in loss of production and system resources, particularly when restarting a productive system. Moreover, the starting of a server and its VMs further adds to the system overhead by increasing memory consumption, which also harms the normal work of the server and server software. The restarting of the server adds overhead in regards to the Central Processing Unit (CPU), as the server would have to start up from scratch.
  • Memory leaks can occur for various reasons, such as due to a clean-up operation failure at a cache that can cause memory leaks. No conventional methods or systems provide for detection of entries or object resulting from the operation failure that cause such memory leaks or the time period during which these entries or objects are used.
  • FIG. 1 illustrates a conventional profiling tool. Client 102 is in communication with server 108. Client 102 includes a VM 102. Server 108 includes a VM 112, which includes Java Virtual Machine Profiling Interface (JVMPI)-based interface 116 and implementation 114. Server 108 further includes a native/default profiling agent (having an agent library) 110 which is plugged into the VM 112 at start-up. Since JVMPI is a native/default-interface, the agent 110 is also written in native code. An agent 110 refers to a software entity, which is used to gather profiling information native VM interfaces (e.g., JVMPI). JVMPI-based implementation 114 suffers from high memory footprints and, like conventional tools JProbe and Wily Introscope, requires a VM restart. However, conventional profiling tools (e.g., also those using Java Virtual Machine Tool Interface (JVMTI)) cannot be used in productive systems without disturbing user sessions. Further, they cannot be used in large application server environments as they cause high memory consumption. Referring back to FIG. 1, for example, to start profiling traces, the VM 112 is to be restarted in special way, such as by having the agent 110 loaded at VM-startup, which can cause negative impact on performance and memory consumption. There are merely some of the limitations of conventional profiling solutions. Similarly, conventional monitoring tools and debugging tools (e.g., using Java Virtual Machine Debugging Interface (JVMDI)) also suffer from these and additional limitations.
  • SUMMARY
  • A system and method are provided for performing loitering trace in virtual machines. In one embodiment, status of objects in a garbage collection heap at a first virtual machine at a server is identified, in response to a memory leak. First objects that are used are identified. Second objects that are alive and not being used are identified. Information regarding the first objects and the second objects is communicated to a second virtual machine at a client.
  • The above attributes may be implemented using a computer program, a method, a system or apparatus, or any combination of computer programs, methods, or systems. These and other details of one or more embodiments of the invention are set forth in the accompanying drawings and in the description below.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • The invention is illustrated by way of example and not by way of limitation in the figures of the accompanying drawings in which like references indicate similar elements. It should be noted that references to “an” or “one” embodiment in this disclosure are not necessarily to the same embodiment, and such references mean at least one.
  • FIG. 1 illustrates a conventional profiling tool.
  • FIG. 2 illustrates an embodiment of a server having an embodiment of an on-demand profiling infrastructure.
  • FIG. 3 illustrates an embodiment of a backend VM having an embodiment of an on-demand profiling infrastructure.
  • FIG. 4 illustrates an embodiment of a process for profiling using an embodiment of an on-demand profiling infrastructure.
  • FIG. 5 illustrates an embodiment of a mechanism for performing loitering trace at a virtual machine.
  • FIG. 6 illustrates an embodiment of a transaction sequence for performing loitering trace.
  • FIG. 7 illustrates an embodiment of a process for performing loitering trace.
  • FIG. 8 illustrates an embodiment of a monitoring tool.
  • FIG. 9 illustrates an embodiment of a computing system.
  • FIG. 10 illustrates an embodiment of a client/server network system employing a message enhancement mechanism.
  • DETAILED DESCRIPTION
  • As used herein, references to one or more “embodiments” are understood as describing a particular feature, structure, or characteristic included in at least one implementation of the invention. Thus, phrases such as “in one embodiment” or “in an alternate embodiment” appearing herein describe various embodiments and implementations of the invention, and do not necessarily all refer to the same embodiment. However, they are also not necessarily mutually exclusive. Descriptions of certain details and implementations follow, including a description of the figures, which may depict some or all of the embodiments described below, as well as discussing other potential embodiments or implementations of the inventive concepts presented herein.
  • Java applications can vary in both size and complexity. In addition, certain large Java application (e.g., ˜10,000 classes and ˜1,000,000 methods with ˜100,000,000 method calls) may run 24/7 (“long living” applications). Within a long living application, major problems (e.g., memory leaks) are expected to occur in terms of both stability and performance. For example, a single long living object that increases in size by 1 byte between each GC cycle will eventually cause the application and VM to crash due to an OOME. Although such a crash may take a long time (e.g., 1 bytes per GC cycle*millions of free bytes of memory), it will inevitably occur. Furthermore, when dealing with such long applications and productive systems, mere use of commercial and non-commercial conventional profiling tools and debugging tools having JVMPI and JVMTI profiling interfaces and JVMDI debugging interface, respectively, are not suitable and cannot provide the necessary profiling, debugging, and monitoring information. Even when dealing with suitable systems, such conventional tools cause high memory footprints and are not effective without having to restart the VM and are known to disturb user sessions inherent to the VM.
  • In one embodiment, a vendor-specific proprietary interface and implementation are provided, as described throughout this document (e.g., see FIG. 1). This implementation can be made an integral part of a VM (e.g., JVM, SAP JVM) and allow for on-demand examining of system problems, including in productive systems, without restarting the underlying VM. These system problems can range anywhere from memory leaks to performance, scalability and synchronization problems. In one embodiment, “on-demand” refers to examining (e.g., profiling, tracing, debugging, and/or monitoring) system problems in runtime, such as without the need for restarting the underlying VM.
  • FIG. 2 illustrates an embodiment of a server 202 having an embodiment of an on-demand profiling infrastructure 208. Sever 202 comprises a backend VM 206 (e.g., JVM, SAP JVM) having an embodiment of an on-demand profiling framework or infrastructure (profiling infrastructure) 208. Profiling infrastructure 208 is shown in communication with a server Java application programming interface (API) 204. In one embodiment, profiling infrastructure 208 is implemented as an intrinsic and direct part of the underlying VM 206 and is embedded within the backend VM 206, rather than relying on native profiling interfaces, such as JVMTI and JVMPI, and agent, for implementation. Since the profiling infrastructure 208 is an intrinsic part of the backend VM 206, no additional memory overhead is needed. Java API 204 allows for starting and stopping of the profiling backend VM 206. In one embodiment, backend VM 206 (e.g., Java VM) resides at server 202 (e.g., J2EE server).
  • In one embodiment, profiling is performed using profiling infrastructure 208 that resides at backend VM 206 that is being profiled. Profiling infrastructure 208 includes a number of components (as described in FIG. 3) to perform trace profiling. In one embodiment, using profiling infrastructure 208, no default profiling agent or default implementations and instances (e.g., JVMPI, JVMTI) are needed or employed. Without having the default agent employed, a direct communication is established between backend VM 206 and frontend VM 214 via server Java API 204 and client Java API 212 and profiling protocol 210. Any number of VMs may be used as backend or frontend VMs. Furthermore, when performing profiling trace in an offline profiling mode, an external profiling file 218 is used to store profiling trace data. Starting and stopping of profiling trace may be performed in a number of ways, such as using a Graphical User Interface (GUI)-based monitoring tool 220. The profiling data is written using various components of profiling infrastructure 208 and displayed to the user using any number of display devices. These display devices may include GUI-based display devices. In one embodiment, using profiling infrastructure 208, on-demand profiling is performed which refers to performing the profiling without restarting the underlying VM 206. Stated differently, the profiling is performed in runtime without any interruptions or restarting of the underlying VM 206.
  • Profiling infrastructure 208 can be used for starting profiling traces for certain users or applications, such as using profiling annotations. Profiling annotations refer to a concept of tagging threads with certain semantic information from an application server environment. Here, Java API 204 is provided which allows for annotating a Java thread with one or more of the following information: user name, application name, request identifier, and session identifier. If profiling traces are started, a thread filter for such information is provided and thus, a profiling trace can be started only a certain user or application. A Java API is also provided on the client-side, such as client Java API 212, that communication with server Java API 204 via a profiling protocol 210. Client 216 includes frontend VM 214, which includes any arbitrary VM that represents a native application that speaks (e.g., in case of online profiling) the profiling protocol 210 and/or knows (e.g., in case of offline profiling) the profiling file format of profiling file 218. Backend VM 206 is the one that is being profiled.
  • It is to be noted that the VMs 206, 214 may not be VMs and instead be any program or application (e.g., a native application or program) that is compatible with the components of and related to the profiling infrastructure 208. For example, the frontend VM 214 is illustrated here merely as an example for brevity and clarity. It is, however, contemplated that a frontend VM 214 or any VM for that matter is not necessary for embodiments of the present invention. For example, in one embodiment, instead of employing a VM 214, any program or application that is compatible with the mechanisms and components described herein is acceptable and functional and can be employed and implemented. Stated differently, for example, any program that can read and speak the described components (e.g., components of profiling infrastructure 208), protocols (e.g., socket communication protocol), APIs (e.g., server- and client-side APIs 204, 212), parameters, profiling files 218, etc., is compatible and can be used instead of a VM, such as the frontend VM 214. This is applicable throughout this document wherever there is mention of a VM 206, 214.
  • The illustrated mechanism 200 provides both an online mechanism for (interactive) profiling and an offline mechanism for (non-interactive) profiling. When starting profiling the backend VM 206, any profiling parameters including the desired mode, e.g., an online or offline mode, are specified. If started in the online mode, the profiling backend VM 206 opens a port and waits for a connection. The profiling frontend VM 214 attach to this connection via the profiling protocol 210 and Java APIs 204, 212. The starting, running, and stopping of profiling and tracing is then performed. In one embodiment, online profiling is performed via internal components, such as Java APIs 204, 212, or external components, such as a monitoring tool (e.g., Java VM monitor) 220. Online profiling may also be performed using a command line, such as java −agentlib:jdwp,transport=dt_socket, address=8000,suspend=n or bin/java −monjdwp:transport=dt_socket,address=8000,server=y. For the offline mode, profiling files 218 are used to store profiling data and a special interface is provided to couple the backend VM 206 with the frontend VM 214 via client Java API 212 to allow for starting and stopping of traces. In some cases, server Java API 204 can also be used to perform offline profiling. Offline profiling may also be performed using monitoring tool 220 and/or using a command line, such as java −XX: +Profiling −XX:+ProfilingAlloationTrace.
  • When the profiling mechanism 200 is started in the offline or non-interactive mode, the profiling information is stored in an external medium 218 (e.g., file system) and can be analyzed after the profiling run. This way, the profiling information may then be used for port-mortem analysis; however, traces can still be started and stopped in an interactive manner. In contrast, the online or interactive mode allows for analyzing the profiling information online. For example, if a class statistic trace has been enabled and a garbage collection happens, the profiling information can be directly accessible through a stream-based interface.
  • Furthermore, to have no performance degradation in case of running in a non-profiling mode (e.g., when no profiling is being performed), VM 206 may maintain a global flag indicating whether profiling is enabled or not. The flag may be requested each time any profiling data is written. For example, a profiling trace for garbage collection events may be implemented in the following way: when a garbage collection is performed, the global profiling flag is checked. If profiling is enabled, the flag is checked to indicate whether garbage collection events are to be profiled. This can also be done via some VM global flags. If the garbage collection trace is enabled, the backend VM 206 may be called to collect the desired data.
  • FIG. 3 illustrates an embodiment of a backend VM 206 having an embodiment of an on-demand profiling infrastructure 208. In one embodiment, profiling infrastructure 208 contains controller framework 302, thread filter 304, buffer framework 306, class filter 308, identification service 310, communication framework 312, object identification service 314, allocation trace module 316, loitering trace module 318, garbage collection trace module 320, and other trace modules 322 to perform other traces.
  • In one embodiment, profiling controller framework 302 is used for starting and stopping profiling runs and traces. Controller framework 302 allows the user to specify profiling options or settings that the user would want to enable. These profiling settings to be applied are divided into distinct areas, such as functional profiling settings and filter settings. The functional profiling settings determine the area to be profiled (e.g., allocation trace, reference trace, etc.), while the filter settings define the validity scope (e.g., user, session, thread, VM, etc.) of the functional profiling settings. For example, an allocation trace can be started for a specified user. Java API and graphical user interface (GUI) are provided in communication with profiling controller framework 302. GUI is used to enable the user to directly specify the desired profiling settings without any system-guidance. Additionally, a wizard-similar interface is provided. GUI also allows for an expert mode and for a wizard-guided mode. Controller framework 302 may include a profiling evaluation module for analyzing a performed profiling run. For example, the Java API can be used for getting the complete low-level profiling information gathered within a corresponding profiling run as well as for getting condensed, problem-oriented profiling information. The condensed profiling information may be used to directly pinpoint various problematic areas. For example, if the user has performed performance analysis using a time-based sampling approach, the Java API may enable a client to directly receive information about the time-consuming methods. The user may view this information via GUI at a display device at the client.
  • Controller framework 302 is used for starting and stopping profiling runs and traces, which includes starting and stopping various profiling options (further described later). For each profiling run the user is free to determine the set of traces to be started. For example, the user may start an allocation trace using the allocation trace module 316 together with a class statistic trace. A user-defined name may be assigned to each non-interactive profiling run and used later on to evaluate the gathered profiling information. Considering interactive profiling runs, the user is able to evaluate the profiling information online and therefore, the profiling information may be available through a stream-based interface.
  • Furthermore, controller framework 302 may be independent of the surrounding application server environment. Stated differently, controller framework 302 refers to the underlying VM 206 currently executing a profiling request (e.g., starting an allocation trace). The corresponding application server infrastructure may be responsible for starting and stopping the desired trace on other VMs. For example, if an allocation trace is started for a certain user session at VM 208, the application server infrastructure accounts for starting the allocation trace in the VMs executing requests for the user session. Controller framework 302 enables the application server infrastructure to specify thread filters 304. A thread filter 304 may contain the following information: client, user, session identifier, request identifier, application name, and component name. On the one hand, controller framework 302 may provide a facility to tag these pieces of information to a thread. On the other hand, if a certain profiling run is to be started, a thread filter 304 is provided. Hence, for example, a trace may be stared only for a certain user. Accordingly, the application server is responsible for setting the current thread state (e.g., client, user, session identifier, etc.). In one embodiment, an application server includes a J2EE server.
  • In one embodiment, the profiling options include functions/cases, such as memory debugging (e.g., memory leak detection), performance analysis, synchronization monitoring, and application debugging (e.g., detecting called methods). These profiling functions further include a number of sub-functions, such as heap dump, coupling of debugging and profiling infrastructure, time-based sampling, memory-based sampling, method statistic, allocation trace, silent allocation trace, allocation statistic trace, loitering trace, garbage collection trace, garbage collection statistic, class statistic trace, permanent generation statistic trace, local garbage collection trace, other traces, such as reference trace, object death trace, object movement trace, global reference trace, method trace, time method trace, input/output (I/O) trace, monitor trace, shared lock trace, method count trace, execution line trace, scheduler trace, and exception trace.
  • Solving a memory leak problem may include a couple of processes, such as identifying the Java classes or objects caused the memory leak, and determining where in the infrastructure or application code the leak occurred. Many of the sub functions can be used to solve memory leak problems. Class statistic trace functionality is provided to help identify the Java classes that cause memory leaks. Class statistic trace includes getting an overview of all living classes within particular VM, including class name, class loader description, the number of object instances, and the accumulated net and gross size of all object instances. The information may be traced after each full local garbage collection. Reference trace includes detecting the objects holding references to leaking objects. It also provides the complete reference chain to a specific object instance. This information may also be available after one full local garbage collection.
  • If the class statistic trace reveals that specific objects are created over and over again, using the allocation trace module 316, the allocation trace may be enabled to check for the exact allocation place. Using the allocation trace module 316, the allocation trace enables the user to specify a class filter 308. Silent allocation trace is a derivate of allocation trace. When an allocation trace is started, each object, which is allocated and adheres to a user-defined class filter 308, is assigned to an object identifier. Although the allocation trace enables the user to get informed about object allocations; the user may not get the information when the corresponding object dies. In that case, object death trace allows the user to check for those objects are garbage collected and no longer alive. Object movement trace makes allows the checking of why certain objects are kept alive, while the allocation trace allows for getting information when certain objects are created.
  • In some cases, memory leaks occur due to the fact that a failed clean-up operation. For example, considering a, at regular intervals, the cache might be cleared. If the clean-up operation were interrupted at the end of the operation (e.g., due to a VM abort exception), most cache entries would probably be deleted; however, some entries might still exist. Thus, a memory leak may be resulted if the cache were not able to remove any of the existing entries. The detection of this kind of memory leak could be difficult, since most object instances of the corresponding class are removed and merely a few exist. Thus, class statistic trace may not be the right choice to detect such a memory leak. One characteristic of this problem is that the memory leak is caused by objects which may not be used any longer. The loitering trace performed via loitering trace module 318 facilitates the detection of objects which are not used for a long time.
  • Various performance problems may be caused by any number of reasons, such as choosing the wrong algorithm for a problem, repeatedly recalculating the same result, excessive allocating of temporary objects, too many I/O operations or transferring too much memory, etc. Profiling helps improving the performance by determining what is it that is to be optimized. Profiling identifies parts of the overall system for which optimization can have an impact on the overall performance. Optimizing a function which only amounts to a miniscule fraction of the overall runtime may not have noticeable benefits. Profiling also determines how the optimization is to be done. Checking for optimization options of those parts that are identified during the first process. Time-based sampling is used to get an overview of methods, which consume the most CPU resources of the application. Time-based sampling works by dumping a stack trace of the currently active thread at regular intervals. Memory-based sampling works analogously to the time-base sampling; however instead of dumping a stack trace in time intervals (Δt), stack trace is sampled after an amount of memory (μM) is allocated on the Java heap. This way, those methods that allocate the largest number of bytes on the Java heap are identified.
  • When time-based sampling shows that a method uses a large amount of time, the reason for this resource consumption might be that a call of the method is expensive or the method is called very often. To find out how many times a particular method was called, method statistic trace may be used. Together with time-based sampling, method statistic trace may also allow for calculating the average runtime of a specific method (e.g., the “cumulative time” divided by the method count). Method trace is used to get more detailed information than method statistic. Time method trace can be used to provide very detailed trace information. Time method trace provides for detecting method calls that (for any number of reasons) take a particularly long time. To see, if garbage collection is properly configured or if a particular problem related to garbage collection exists, local GC statistic is used, which includes dumping a statistical entry for each local garbage collection (partial and full) for each garbage collection run.
  • Another source of performance problems is related to I/O. These I/O-related problems include a network connection being operated at its bandwidth maximum, the latency being too high, an external system being overloaded, etc. To check for an I/O problem, I/O trace allows for tracing the timing of each I/O operation. I/O trace can be used in analysis to check for operations, where huge amounts of data were transmitted, the I/O operation took an extraordinary amount of time, or a huge amount of small I/O operations was performed.
  • Java has an explicit support for multithreading and concurrency at the language level. Although these welcome features, the typical problems with multithreading and concurrency are deadlocks, race conditions, thread starvation, and scalability problems. Synchronization monitoring is provided to detect such problems. For example, synchronization monitoring includes monitor trace that identifies deadlock or scalability problems and gathers information about locks used inside a VM. To find synchronization problems, a thread trying to acquire a lock is identified and once it is identified, the lock is freed by the thread. Shared lock trace is used to identify deadlocks between VMs and scalability problems of a server instance. Shared lock trace provides information about different kinds of shared lock activities, like entering and leaving. Further, for such problems above, scheduler trace is used to know why a thread was scheduled and why it gave up control of the CPU, and for how long the entire VM was waiting on external I/O or just sleeping.
  • In one embodiment, application debugging is used to provide those the debugging functionalities that are not supported by conventional debugging instances and protocols, such as JVMDI, Java Debug Wire Protocol (JDWP), etc. For example, application debugging covers functionalities, such as call coverage and line coverage. Regarding call coverage, method count trace may deliver a number of calls to a method. Regarding line coverage, execution line trace may deliver information about code lines that were executed. Method call trace is used to find all methods that are called. When the method call trace is enabled, the VM 206 counts method calls and when the method call trace is disabled, the VM 206 dumps the collected information, such as name and signature of a method and the number of times it was called. Execution line trace may be used to find out the lines of code that are not executed. When the execution line trace is triggered, it enables the VM to write out information about the method and code line each time a byte code is interpreted and/or the line number changes. Such information can help the developer find out the lines of code that are not covered particular test cases.
  • Method trace may be employed to trace or profile the debugging process of an application. For example, the method trace is used to find out what has happened before the program reaches a certain point. Such information may be used to trace back the program flow and find out in which way the program reached that point of code. Exception trace is another functionality that may be employed to trace or profile the debugging process of an application. This information can be used to trace back the reasons for exceptions that followed up and for different execution branches.
  • In one embodiment, a dedicated Java API and a GUI is provided to allow for starting and stopping of various functionalities and uses (e.g., allocation trace, loitering trace, GC trace, and other traces) and for getting the corresponding profiling and tracing results. To determine and analyze the profiling and tracing results, an expert mode and/or a guided mode are provided. For example, a guided mode may directly pinpoint any problem areas.
  • Profiling infrastructure 208 is compatible with multiple clients. For example, depending on the surrounding application server infrastructure and whether any clients are handled in a special way, the profiling infrastructure 208 may perform in compliance with several clients, simultaneously, and remain multiple client-compliant. Profiling infrastructure 208 also allows for restricting profiling runs to certain clients, while the surrounding application server environment may assure that the current client information is assigned to the respective thread. Furthermore, profiling infrastructure 208 may be started on-demand, which includes performing profiling infrastructure functionalities (e.g., profiling, tracing, etc.) without restarting the entire application server or even the underlying VM 206. If no profiling option is enabled by a certain user, there is no impact on the response time caused by the profiling infrastructure 208. However, if profiling is enabled, it may depend on the started profiling options and filter settings about how the overall system performance is influenced. For example, if a method trace is started on an application server without any filter settings (e.g., user, classes, etc.), the performance may decrease to an extent. Therefore, the profiling infrastructure 208 as well as the application server infrastructure must provide options to restrict profiling runs. This way, profiling may be enabled for a particular user or session, while users and sessions remain unaffected. In addition, profiling infrastructure 208 provides reasonable and necessary filter settings for various profiling traces.
  • Class filters 308 are implemented to allow for limiting profiling trace outputs by limiting the process of profiling to, for example, specific traces. For example, if a developer seeks to profile only Java object allocations which refer to java.lang.HashMap instances, then, using class filters 308, a profiling allocation trace with a class filter applying exclusively to java.lang.HashMap instances is started. Thread filters 304 relate to profiling annotations (e.g., specifying annotations), such as when an allocation trace exists. Thread filters 304 may also be used by the user to specify when and/or where a trace is to be triggered and/or used. Buffer framework 306 is used to compress and decompress any type of data or information that is being communicated, stored, etc. Communication framework 312 is used to facilitate communication of any data or information between and within various components, elements, modules, systems, servers, VM, etc. Communication framework 312 is also used to determine and facilitate the storing of data or information, such as storing the data using files or socket connections.
  • ID service 310 is employed to specify variables, such a class, a name of the class, etc. to assign identification to them. Once class, class names, etc. are assigned an ID (e.g., a number), they are then mapped with each other and with various components and variables via a mapping packet, instead of mapping by names. Using ID service 310, the same can be done with threads and methods. For example, by assigning IDs (instead of names) to threads and methods, when dumping is performed, the IDs of threads and methods are dumped rather than their names. This technique of using IDs (e.g., numbers) instead of using the names is efficient, fast, and saves memory.
  • For example, an allocation event is considered. ID numbers are mapped to various packet names, such as java.Hashtable is mapped to “2000”, the thread (named, “main”) is assigned “3”, and the user (named, “Hansi”) is assigned “7”. Stack trace is then commenced using command lines, such as com.sap.test (line 30), com.sap.submethod (line 2003), etc. The even information may then be provided as 2000, 3, etc. It is known that ID number 2000 was mapped to the underlying hashtable, while ID number 3 was mapped to the thread. Using these ID's, names (e.g., main, Hansi, etc.) are not needed and instead, IDs are used, which provides an easier technique for packet name mapping. Similarly, object ID service 314 is used to assign IDs (e.g., numbers) to objects so the IDs can be used to, for example, identify and compare the objects, instead of using object names.
  • In one embodiment, profiling information and any other relevant data is displayed at a display device via GUI at a client so that a user can access and evaluate the displayed information. The information may also be stored at a database and/or file system for subsequent retrieval and analysis. Although Java components, such as J2EE server, Java VM, Java heap, and Java memory errors, etc., are discussed here for simplicity and brevity, it should be noted, however, that the underlying principles and embodiments of the present invention may be implemented within any type of object-oriented and runtime environments. Moreover, it should be noted that requirements and examples used in this document do not necessarily reflect the real values that a system or program would actually produce. For example, garbage collection may be invoked multiple times while checking the VM heap memory size, so that there are different VM implementations and, according to a relevant VM specification, a given VM implementation might not clean up the memory immediately after it has been requested to do so. Thus, to be sure that a memory cleanup is provoked, the memory size may be checked and the garbage collection may be invoked again, as necessary.
  • Garbage collection as described here includes a process designed to identify and reclaim blocks of memory that are dispensed by a memory allocator but are no longer “alive” or “live” (e.g., no longer being used, as determined, for example, by not being reachable from any currently referenced objects or entities). Garbage collection can sometimes be handled as a background task by runtime systems rather than as an explicit task by user programs. Garbage collection can also be handled as an inlined task. Garbage collection can be used to reclaim memory in runtime systems, and there are some well-known garbage collection algorithms (e.g., reference counting, mark-sweep, mark-compact, and copying algorithms).
  • A VM (e.g., VM 206) is an example of a runtime system. A VM refers to an abstract machine that includes an instruction set, a set of registers, a stack, a heap, and a method area, such as a machine or processor. A VM essentially acts as an interface between program code and the actual processor or hardware platform on which the program code is to be executed. The program code includes instructions from the VM instruction set that manipulates the resources of the VM. The VM executes instructions on the processor or hardware platform on which the VM is running, and manipulates the resources of that processor or hardware platform, so as to effect the instructions of the program code. For example, a Java source program can be compiled into program code, such as bytecode. Bytecode can be executed on a VM, such as JVM, running on any processor or platform. The JVM can either interpret the bytecode one instruction at a time, or the bytecode can be further compiled for the real processor or platform using a just-in-time (JIT) compiler.
  • The illustrated VM 206 includes a JVM (e.g., SAP JVM), which is used as an example; however, other examples of VMs, which can be used in various embodiments, include Advanced Business Application Programming (ABAP) language VMs, Common Language Runtime (CLR) VMs, and the like. ABAP is a programming language for developing applications for SAP systems, such as SAP R/3 system, which is a widely installed business application system developed by SAP AG of Walldorf, Germany. The CLR is a managed code execution environment developed by Microsoft Corp. of Redmond, Wash. For simplicity and brevity, the discussion in this document focuses on virtual machines, and in particular Java virtual machine 104, but it is to be understood that the techniques described herein can also be used with other types of runtime systems.
  • A runtime system includes a code execution environment that executes instructions or code in user requests and that provides runtime services for that code. Code runtime services may include functionality, such as process, thread, and memory management (e.g., laying out objects in the server memory, sharing objects, managing references to objects, and garbage collecting objects). Enhanced runtime services may include functionality, such as error handling and establishing security and connectivity.
  • The illustrated server 202 includes a J2EE server/engine/node, which supports Enterprise Java Bean (“EJB”) components and EJB containers (at the business layer) and Servlets and Java Server Pages (“JSP”) (at the presentation layer). It is understood that processes taught by the discussion above can be practiced within various software environments such as, for example, object-oriented and non-object-oriented programming environments, Java based environments (such as a J2EE environment or environments defined by other releases of the Java standard), other environments (e.g., a .NET environment, a Windows/NT environment each provided by Microsoft Corporation), and the like.
  • FIG. 4 illustrates an embodiment of a process for profiling using an embodiment of an on-demand profiling infrastructure. At decision block 402, whether profiling need be started is determined. If not, the non-profiling mode is maintained, which saves system resources. If the profiling is to be started, the profiling mechanism is switched to the profiling mode at processing block 408. At decision block 410, a determination is made as to whether online or offline profiling is to be performed. If the profiling is to be performed online, the online profiling mode is triggered for, for example, interactive profiling at processing block 412.
  • Online profiling is started via Java APIs (e.g., server and client Java APIs), using monitoring tools (e.g., Java VM monitor), and/or using command lines. If the profiling is to be performed offline, the offline profiling mode is triggered for, for example, non-interactive profiling. Offline profiling can also be started using monitoring tools, using command lines, and/or via Java APIs as described above. At processing block 420, any profiling data obtained from offline profiling is stored at an external source, such as a profiling file. The data at the profiling file may be stored in zipped format. At decision block 414, whether the process of profiling be continued is determined. If yes, the profiling continues with decision block 410. If not, the profiling status is switched to the non-profiling mode at processing block 416. This non-profiling mode is maintained at processing block 406.
  • FIG. 5 illustrates an embodiment of a mechanism 500 for performing loitering trace at a virtual machine 206. Memory leaks can occur for a number of reasons, such as, in many cases, memory leaks can occur due to the failing of a clean-up operation at a cache. For example, a cache is to be cleared at regular intervals, and if a clean-up operation is to be interrupted even at the end of the clean-up operation, some entries or objects may still exist and not be detected. These entries or objects can cause a memory leak if the cache were not able to remove such entries. Sometimes, even a follow up clean-up operation may not clear the entries remaining from the previous operation. In one embodiment, such objects or entries that are not to be used anymore, but remain in the VM, and can cause memory leaks and are detected using an embodiment of loitering trace. A clean-up operation refers to a process being performed at a VM 206 to remove the old and unused applications and substitute them with new applications. The old or unused applications may not be removed due to programming errors or resource failures. It is contemplated that clean-up operation is used here only as an example for brevity and that loitering trace can be used for to cure and/or prevent any number of memory leak causes.
  • In one embodiment, mechanism 500 includes a server 202 having a VM 206 (e.g., backend VM) that is the underlying VM and is being profiled. VM 206 includes a profiling infrastructure having a loitering trace module 318 and a hook 502. VM 206 is in communication with another VM 214 (e.g., backend VM 214) that resides at a client 216 in communication with the server 202. In one embodiment, loitering trace module 318 is used to collect information about unused objects at the VM 206 (e.g., objects in the old generation) and generate statistics regarding the collected information. Hook 502 is employed at the VM 206 to notify of the profiling infrastructure 208 each time an object is used, which includes whether (1) a method of this object is invoked; (2) a field of this object is read to written; and (3) this object is used for synchronization. If the loitering trace is enabled, the object is marked as used. This information includes identifying (1) objects that are being used and are alive, (2) objects that are not being used but are alive; and (3) objects that are dead and are not being used and are to be removed from the garbage collection process. A report including such information is then generated using the profiling infrastructure 208 to be sent to the requesting VM 214 at client 216. It is contemplated that objects may not be removed from the heap but rather, they are remembered and reported.
  • It is contemplated that any references to Java-based components, such as Java application server, Java VMs 206, 214, Java objects, etc., are provided as examples and that the mechanism 500 and other embodiments of the present invention can also be used with other non-Java-based environments and components. Furthermore, a backend VM 206 may reside at the same J2EE engine as the tree generation module or at another J2EE engine or at another server. A server and a client may include servers and clients similar to server 202 and client 216, respectively, of FIG. 2.
  • FIG. 6 illustrates an embodiment of a transaction sequence for performing loitering trace. In one embodiment, loitering trace request is initiated 602 via a client 602. Loitering trace is initiated 604 via loitering trace module 318 at profiling infrastructure 208. Further, at profiling infrastructure 208, using hook, each time an object at a VM is used, whether the loitering trace is enabled is checked 606. If the loitering trace is enabled, the used object is marked in the heap. If the loitering trace is not enabled, the process ends and nothing further occurs. Client 602 request stopping of the loitering trace 608. Loitering trace stops 610. Further, the heap is iterated and any not used (not marked) objects are found 612. The list of such objects is returned 614 to the client 602.
  • FIG. 7 illustrates an embodiment of a process for performing loitering trace. A memory leak is detected at processing block 702. Loitering trace is started at processing block 704. The loitering trace is now running at processing block 706 and each time an object is used, it is marked as used at processing block 708. When the trace is stopped at processing block 710, a report of all objects (including objects that are alive and being used, objects alive and not being used, objects dead and not being used, etc.) is formed at processing block 712. At processing block 714, the report is communicated to one or more clients for one or more users to access, use, and evaluate. In one embodiment, when the loitering trace is enabled, it marks those objects that are used while it is enabled. When the loitering trace is stopped, the objects that were during the time the trace was active are now marked. Using this technique, which objects were used and which objects were not used are determined and a report stating that is formed and provided. To provide these results, the loitering trace is to be running at some time for a certain amount of time.
  • FIG. 8 illustrates an embodiment of a monitoring tool 220. The illustrated monitoring tool 220 includes a JVM monitoring tool (e.g., SAP JVM monitoring tool). Monitoring tool 220 includes a menu line 802 that includes various menu items, such as command, debugging, etc. Monitoring tool 220 further provides a list of VMs 804, and details about any VM 806 which includes a number of items, such as performance, VM info, debugging, trace flags, local memory, etc. Monitoring tool 220 further provides shared memory configuration 808 and shared memory state 810. Monitoring tool 220 is illustrated merely as an example and the embodiment of the present invention are in no way limited to using the illustrated monitoring tool 220.
  • Processes taught by the discussion above may be performed with program code, such as machine-executable instructions, which can cause a machine (such as a “virtual machine”, a general-purpose processor disposed on a semiconductor chip, a special-purpose processor disposed on a semiconductor chip, etc.) to perform certain functions. Alternatively, these functions may be performed by specific hardware components that contain hardwired logic for performing the functions, or by any combination of programmed computer components and custom hardware components.
  • One or more modules, components, or elements described throughout this document, such as the ones shown within or associated with the on-demand profiling infrastructure 206 of profiling mechanism 200 of FIG. 2, may include hardware, software, and/or a combination thereof. In a case where a module includes software, the software data, instructions, and/or configuration may be provided via an article of manufacture by a machine/electronic device/hardware. An article of manufacture may include a machine accessible/readable medium having content to provide instructions, data, etc. The content may result in an electronic device, for example, a filer, a disk, or a disk controller as described herein, performing various operations or executions described. A machine accessible medium includes any mechanism that provides (i.e., stores and/or transmits) information/content in a form accessible by a machine (e.g., computing device, electronic device, electronic system/subsystem, etc.). For example, a machine accessible medium includes recordable/non-recordable media (e.g., read only memory (ROM), random access memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, etc.), as well as electrical, optical, acoustical or other form of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.), etc. The machine accessible medium may further include an electronic device having code loaded on a storage that may be executed when the electronic device is in operation. Thus, delivering an electronic device with such code may be understood as providing the article of manufacture with such content described above. Furthermore, storing code on a database or other memory location and offering the code for download over a communication medium via a propagated signal may be understood as providing the article of manufacture with such content described above. The code may also be downloaded from a remote computer (e.g., a server) to a requesting computer (e.g., a client) by way of data signals embodied in a propagation medium (e.g., via a communication link (e.g., a network connection)).
  • FIG. 9 illustrates an embodiment of a computing system 900. Computing system 900 may be used for implementing one or more embodiments of the present invention and for executing program code stored by an article of manufacture. It is important to recognize that the computing system 900 represents merely of various computing system architectures that can be used for the same purposes. The applicable article of manufacture may include one or more fixed components (such as hard disk drive 902 or memory 906) and/or various movable components, such as compact disk (CD) ROM 904, a compact disc, a magnetic tape, and the like. To execute the program code, typically instructions of the program code are loaded into RAM 906. Then, processing core 908 executes the instructions. A processing core may include one or more processors and a memory controller function. A virtual machine or “interpreter” (e.g., JVM) may run on top of the processing core (architecturally speaking) to convert abstract code (e.g., Java bytecode) into instructions that are understandable to the specific processor(s) of processing core 908. Computing system 900 further includes network interface 910 and bus 912 to connect to other systems via a network and to have various components communicate with each other, respectively.
  • FIG. 10 illustrates an embodiment of a client/server network system 1000 employing an on-demand profiling infrastructure 1018. As illustrated, network 1008 links server 1010 with client systems 1002-1006. Server 1010 includes programming data processing system suitable for implementing apparatus, programs, and/or methods in accordance with one or more embodiments of the present invention. Server 1010 includes processor 1012 and memory 1014. Server 1010 provides a core operating environment for one or more runtime systems (e.g., VM 1016) at memory 1014 to process user requests. Memory 1014 may include a shared memory area that is accessible by multiple operating system processes executing in server 1010. For example, VM 1016 may include an enterprise server (e.g., a J2EE-compatible server or node, Web Application Server developed by SAP AG, WebSphere Application Server developed by IBM Corp. of Armonk, N.Y., and the like). The enterprise server at VM 1016 may host the on-demand profiling infrastructure 1018. Memory 1014 can be used to store an operating system, a Transmission Control Protocol/Internet Protocol (TCP/IP) stack for communicating over network 1008, and machine executable instructions executed by processor 1012. In some embodiments, server 1010 may include multiple processors, each of which can be used to execute machine executable instructions.
  • Client systems 1002-1006 may execute multiple application or application interfaces. Each instance or application or application interface may constitute a user session. Each user session may generate one or more requests to be processed by server 1010. The requests may include instructions or code to be executed on a runtime system, such as VM 1016, on server 1010, such as the requests made via the on-demand profiling infrastructure 1018 and its components and modules as described throughout this document.
  • In addition to what is described herein, various modifications may be made to the disclosed embodiments and implementations of the invention without departing from their scope. Therefore, the illustrations and examples herein should be construed in an illustrative, and not a restrictive sense. The scope of the invention should be measured solely by reference to the claims that follow.

Claims (20)

1. A method comprising:
identifying status of objects in a garbage collection heap at a first virtual machine at a server, in response to a memory leak;
identifying first objects that are being used;
identifying second objects that are alive and not being used;
communicating a report having information relating to the first objects and the second objects to a second virtual machine at a client.
2. The method of claim 1, further comprising generating the report having the information relating to the first objects and the second objects.
3. The method of claim 1, further comprising removing the second objects that are alive and not being used.
4. The method of claim 1, further comprising preserving the first objects that are being used, the first objects are alive.
5. The method of claim 1, wherein the garbage collection heap includes a Java garbage collection heap.
6. The method of claim 1, wherein the server comprises a Java application server having a Java 2 Enterprise Edition (J2EE) engine, the J2EE engine having the first virtual machine, the first virtual machine including a first Java virtual machine.
7. The method of claim 1, wherein the second virtual machine comprises a second Java virtual machine.
8. A system comprising:
a server having a first virtual machine, the first virtual machine having a profiling infrastructure, the profiling infrastructure to
identify status of objects in a garbage collection heap at a first virtual machine at a server, in response to a memory leak,
identify first objects that are being used,
identify second objects that are alive and not being used,
communicate a report having information regarding the removing of the first objects and the second objects to a second virtual machine at a client; and
a client coupled with the server, the client having a second virtual machine to receive the report from the first virtual machine at the server.
9. The system of claim 8, wherein the profiling infrastructure is further to generate the report having the information relating to the first objects and the second objects.
10. The system of claim 8, wherein the server to remove the second objects that are alive and not being used.
11. The system of claim 8, wherein the server to preserve the first objects that are being used, the first objects are alive.
12. The system of claim 8, wherein the garbage collection heap includes a Java garbage collection heap.
13. The system of claim 8, wherein the server comprises a Java application server having a Java 2 Enterprise Edition (J2EE) engine, the J2EE engine having the first virtual machine, the first virtual machine including a first Java virtual machine.
14. The system of claim 8, wherein the second virtual machine comprises a second Java virtual machine.
15. A machine-readable medium comprising instructions, which when executed, cause the machine to:
identify status of objects in a garbage collection heap at a first virtual machine at a server, in response to a memory leak;
identify first objects that are used;
identify second objects that are alive and not being used;
communicate information regarding the removing of the first objects and the second objects to a second virtual machine at a client.
16. The machine-readable medium of claim 15, wherein the instructions when executed, further cause the machine to generate the report having the information relating to the first objects and the second objects.
17. The machine-readable medium of claim 15, wherein the instructions when executed, further cause the machine to remove the second objects that are alive and not being used.
18. The machine-readable medium of claim 15, wherein the instructions when executed, further cause the machine to preserve the first objects that are being used, the first objects are alive.
19. The machine-readable medium of claim 15, wherein the information includes information regarding the third objects being preserved in the heap.
20. The machine-readable medium of claim 15, wherein the garbage collection heap includes a Java garbage collection heap.
US11/731,499 2007-03-30 2007-03-30 Method and system for providing loitering trace in virtual machines Abandoned US20080243970A1 (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
US11/731,499 US20080243970A1 (en) 2007-03-30 2007-03-30 Method and system for providing loitering trace in virtual machines
US12/638,500 US7971010B2 (en) 2007-03-30 2009-12-15 Mechanism for performing loitering trace of objects that cause memory leaks in a post-garbage collection heap

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
US11/731,499 US20080243970A1 (en) 2007-03-30 2007-03-30 Method and system for providing loitering trace in virtual machines

Related Child Applications (1)

Application Number Title Priority Date Filing Date
US12/638,500 Division US7971010B2 (en) 2007-03-30 2009-12-15 Mechanism for performing loitering trace of objects that cause memory leaks in a post-garbage collection heap

Publications (1)

Publication Number Publication Date
US20080243970A1 true US20080243970A1 (en) 2008-10-02

Family

ID=39796169

Family Applications (2)

Application Number Title Priority Date Filing Date
US11/731,499 Abandoned US20080243970A1 (en) 2007-03-30 2007-03-30 Method and system for providing loitering trace in virtual machines
US12/638,500 Active US7971010B2 (en) 2007-03-30 2009-12-15 Mechanism for performing loitering trace of objects that cause memory leaks in a post-garbage collection heap

Family Applications After (1)

Application Number Title Priority Date Filing Date
US12/638,500 Active US7971010B2 (en) 2007-03-30 2009-12-15 Mechanism for performing loitering trace of objects that cause memory leaks in a post-garbage collection heap

Country Status (1)

Country Link
US (2) US20080243970A1 (en)

Cited By (26)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080243969A1 (en) * 2007-03-30 2008-10-02 Sap Ag Method and system for customizing allocation statistics
US20080243968A1 (en) * 2007-03-30 2008-10-02 Sap Ag Method and system for object age detection in garbage collection heaps
US20080244547A1 (en) * 2007-03-30 2008-10-02 Sap Ag Method and system for integrating profiling and debugging
US20080244531A1 (en) * 2007-03-30 2008-10-02 Sap Ag Method and system for generating a hierarchical tree representing stack traces
US20080244546A1 (en) * 2007-03-30 2008-10-02 Sap Ag Method and system for providing on-demand profiling infrastructure for profiling at virtual machines
US20100095280A1 (en) * 2007-03-30 2010-04-15 Ralf Schmelter Method and system for providing loitering trace in virtual machines
US20110138366A1 (en) * 2009-12-04 2011-06-09 Sap Ag Profiling Data Snapshots for Software Profilers
US20110138365A1 (en) * 2009-12-04 2011-06-09 Sap Ag Component statistics for application profiling
US20110138363A1 (en) * 2009-12-04 2011-06-09 Sap Ag Combining method parameter traces with other traces
US20110138385A1 (en) * 2009-12-04 2011-06-09 Sap Ag Tracing values of method parameters
US20120159454A1 (en) * 2010-12-20 2012-06-21 Microsoft Corporation Probe insertion via background virtual machine
US20130227529A1 (en) * 2013-03-15 2013-08-29 Concurix Corporation Runtime Memory Settings Derived from Trace Data
US8667471B2 (en) 2007-03-30 2014-03-04 Sap Ag Method and system for customizing profiling sessions
US20160011893A1 (en) * 2014-07-11 2016-01-14 Beeman C. Strong Managing generated trace data for a virtual machine
US20160323160A1 (en) * 2015-04-29 2016-11-03 AppDynamics, Inc. Detection of node.js memory leaks
US9575874B2 (en) 2013-04-20 2017-02-21 Microsoft Technology Licensing, Llc Error list and bug report analysis for configuring an application tracer
US9658936B2 (en) 2013-02-12 2017-05-23 Microsoft Technology Licensing, Llc Optimization analysis using similar frequencies
US9767006B2 (en) 2013-02-12 2017-09-19 Microsoft Technology Licensing, Llc Deploying trace objectives using cost analyses
US9772927B2 (en) 2013-11-13 2017-09-26 Microsoft Technology Licensing, Llc User interface for selecting tracing origins for aggregating classes of trace data
US9804949B2 (en) 2013-02-12 2017-10-31 Microsoft Technology Licensing, Llc Periodicity optimization in an automated tracing system
US9864672B2 (en) 2013-09-04 2018-01-09 Microsoft Technology Licensing, Llc Module specific tracing in a shared module environment
US9965375B2 (en) 2016-06-28 2018-05-08 Intel Corporation Virtualizing precise event based sampling
US10178031B2 (en) 2013-01-25 2019-01-08 Microsoft Technology Licensing, Llc Tracing with a workload distributor
EP3819763A4 (en) * 2018-07-27 2021-08-25 Samsung Electronics Co., Ltd. Electronic device and operating method thereof
US11182272B2 (en) * 2018-04-17 2021-11-23 International Business Machines Corporation Application state monitoring
US11934853B2 (en) 2018-07-27 2024-03-19 Samsung Electronics Co., Ltd. Electronic device and operating method thereof

Families Citing this family (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102007030396B4 (en) * 2007-06-29 2014-11-27 Trumpf Werkzeugmaschinen Gmbh + Co. Kg Device for controlling a machine and remote communication system
US8959442B2 (en) * 2010-06-11 2015-02-17 Microsoft Corporation Memory allocation visualization for unmanaged languages
US8667472B1 (en) * 2010-07-29 2014-03-04 Disney Enterprises, Inc. System and method of instrumenting code for in-production monitoring
US9122805B2 (en) * 2012-10-02 2015-09-01 International Business Machines Corporation Resilient mock object creation for unit testing
US9348929B2 (en) 2012-10-30 2016-05-24 Sap Se Mobile mapping of quick response (QR) codes to web resources
US9003233B2 (en) * 2012-12-31 2015-04-07 Bmc Software, Inc. Memory leak detection
US8843901B2 (en) * 2013-02-12 2014-09-23 Concurix Corporation Cost analysis for selecting trace objectives
US20130283102A1 (en) * 2013-02-12 2013-10-24 Concurix Corporation Deployment of Profile Models with a Monitoring Agent
US9355029B2 (en) 2013-06-28 2016-05-31 Sap Se Thread-based memory management with garbage collection
US9213530B2 (en) 2013-08-15 2015-12-15 Oracle International Corporation Runtime memory throttling
US10114745B2 (en) 2014-10-07 2018-10-30 Red Hat, Inc. Assisted garbage collection in a virtual machine
US9921859B2 (en) * 2014-12-12 2018-03-20 The Regents Of The University Of Michigan Runtime compiler environment with dynamic co-located code execution
CN105980978B (en) * 2014-12-13 2019-02-19 上海兆芯集成电路有限公司 For detecting the logic analyzer of pause
US9720659B2 (en) 2015-02-12 2017-08-01 International Business Machines Corporation Sparse object instantiation
US10540258B2 (en) 2017-07-17 2020-01-21 Sap Se Providing additional stack trace information for time-based sampling in asynchronous execution environments

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050240641A1 (en) * 2003-05-09 2005-10-27 Fujitsu Limited Method for predicting and avoiding danger in execution environment
US7086064B1 (en) * 2000-05-27 2006-08-01 International Business Machines Corporation Performance profiling tool
US20070027942A1 (en) * 2005-07-27 2007-02-01 Trotter Martin J Memory leak detection
US20080209404A1 (en) * 2007-02-27 2008-08-28 International Business Machines Corporation Method and system for analyzing memory leaks occurring in java virtual machine data storage heaps

Family Cites Families (66)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6158024A (en) * 1998-03-31 2000-12-05 International Business Machines Corporation Method and apparatus for structured memory analysis of data processing systems and applications
US6055492A (en) * 1997-12-12 2000-04-25 International Business Machines Corporation System and method for providing trace information data reduction
US6002872A (en) * 1998-03-31 1999-12-14 International Machines Corporation Method and apparatus for structured profiling of data processing systems and applications
EP1049979B1 (en) * 1997-12-19 2003-05-02 Microsoft Corporation Incremental garbage collection
US6093216A (en) * 1998-05-29 2000-07-25 Intel Corporation Method of run-time tracking of object references in Java programs
GB9813266D0 (en) * 1998-06-20 1998-08-19 Koninkl Philips Electronics Nv Stored data object marking for garbage collectors
EP1135727A1 (en) * 1998-11-25 2001-09-26 Sun Microsystems, Inc. A method for enabling comprehensive profiling of garbage-collected memory systems
US6308319B1 (en) * 1999-02-22 2001-10-23 Sun Microsystems, Inc. Thread suspension system and method using trapping instructions in delay slots
AU1405100A (en) * 1999-12-15 2001-06-25 Sun Microsystems, Inc. Open debugging environment
US6226653B1 (en) * 2000-01-10 2001-05-01 International Business Machines Corporation Method and apparatus for performing generational garbage collection using remembered set counter
US6826583B1 (en) * 2000-05-15 2004-11-30 Sun Microsystems, Inc. Local allocation buffers for parallel garbage collection
US6839725B2 (en) * 2000-05-16 2005-01-04 Sun Microsystems, Inc. Dynamic adaptive tenuring of objects
US6799191B2 (en) * 2000-05-16 2004-09-28 Sun Microsystems, Inc. Object sampling technique for runtime observations of representative instances thereof
US6957237B1 (en) * 2000-06-02 2005-10-18 Sun Microsystems, Inc. Database store for a virtual heap
US6658652B1 (en) * 2000-06-08 2003-12-02 International Business Machines Corporation Method and system for shadow heap memory leak detection and other heap analysis in an object-oriented environment during real-time trace processing
US6971097B1 (en) * 2000-06-09 2005-11-29 Sun Microsystems, Inc. Method and apparatus for implementing concurrently running jobs on an extended virtual machine using different heaps managers
US6662362B1 (en) * 2000-07-06 2003-12-09 International Business Machines Corporation Method and system for improving performance of applications that employ a cross-language interface
GB0022131D0 (en) * 2000-09-09 2000-10-25 Ibm Data sorting in information storage systems
US6920541B2 (en) * 2000-12-21 2005-07-19 International Business Machines Corporation Trace termination for on-the-fly garbage collection for weakly-consistent computer architecture
US6795836B2 (en) * 2000-12-29 2004-09-21 International Business Machines Corporation Accurately determining an object's lifetime
CA2350735A1 (en) * 2001-03-14 2002-09-14 Ibm Canada Limited-Ibm Canada Limitee A method for providing open access to application profiling data
US7350194B1 (en) * 2001-09-24 2008-03-25 Oracle Corporation Techniques for debugging computer programs involving multiple computing machines
CN100375013C (en) * 2002-04-08 2008-03-12 国际商业机器公司 Method and system for problem determination in distributed enterprise applications
CA2391719A1 (en) * 2002-06-26 2003-12-26 Ibm Canada Limited-Ibm Canada Limitee Editing files of remote systems using an integrated development environment
CA2391733A1 (en) 2002-06-26 2003-12-26 Ibm Canada Limited-Ibm Canada Limitee Framework to access a remote system from an integrated development environment
US7174354B2 (en) * 2002-07-31 2007-02-06 Bea Systems, Inc. System and method for garbage collection in a computer system, which uses reinforcement learning to adjust the allocation of memory space, calculate a reward, and use the reward to determine further actions to be taken on the memory space
US6792460B2 (en) * 2002-10-02 2004-09-14 Mercury Interactive Corporation System and methods for monitoring application server performance
US7010555B2 (en) * 2002-10-17 2006-03-07 International Business Machines Corporation System and method for compacting a computer system heap
US7035884B2 (en) * 2002-11-05 2006-04-25 Sun Microsystems, Inc. Placement of allocation trains in the train algorithm
US7143124B2 (en) * 2002-12-06 2006-11-28 Sun Microsystems, Inc. Detection of dead regions during incremental collection
US7031990B2 (en) * 2002-12-06 2006-04-18 Sun Microsystems, Inc. Combining external and intragenerational reference-processing in a garbage collector based on the train algorithm
US7293263B2 (en) * 2002-12-20 2007-11-06 Bea Systems, Inc. System and method for memory leak detection in a virtual machine environment
US7275239B2 (en) * 2003-02-10 2007-09-25 International Business Machines Corporation Run-time wait tracing using byte code insertion
US7114150B2 (en) * 2003-02-13 2006-09-26 International Business Machines Corporation Apparatus and method for dynamic instrumenting of code to minimize system perturbation
US7509644B2 (en) * 2003-03-04 2009-03-24 Secure 64 Software Corp. Operating system capable of supporting a customized execution environment
US7386686B2 (en) * 2003-03-28 2008-06-10 Intel Corporation Inlining with stack trace cache-based dynamic profiling
US7293260B1 (en) * 2003-09-26 2007-11-06 Sun Microsystems, Inc. Configuring methods that are likely to be executed for instrument-based profiling at application run-time
US20050081190A1 (en) * 2003-09-30 2005-04-14 International Business Machines Corporation Autonomic memory leak detection and remediation
US7577943B2 (en) * 2003-10-24 2009-08-18 Microsoft Corporation Statistical memory leak detection
US7458078B2 (en) * 2003-11-06 2008-11-25 International Business Machines Corporation Apparatus and method for autonomic hardware assisted thread stack tracking
US7275241B2 (en) * 2003-11-21 2007-09-25 International Business Machines Corporation Dynamic instrumentation for a mixed mode virtual machine
US7421698B2 (en) * 2003-12-22 2008-09-02 Sun Microsystems, Inc. System and method for dynamically and persistently tracking incremental profiling data in a process cloning application environment
US7917898B2 (en) * 2004-02-02 2011-03-29 Intel Corporation Methods and apparatus to provide a modular native method invocation system
US20050198088A1 (en) * 2004-03-03 2005-09-08 Sreenivas Subramoney Method and system for improving the concurrency and parallelism of mark-sweep-compact garbage collection
US7428560B1 (en) * 2004-03-12 2008-09-23 Sun Microsystems, Inc. Age segregation for garbage collector
US7359831B2 (en) * 2004-05-21 2008-04-15 Bea Systems, Inc. Diagnostic context
US7325106B1 (en) * 2004-07-16 2008-01-29 Sun Microsystems, Inc. Method for monitoring heap for memory leaks
US7676801B1 (en) * 2004-08-31 2010-03-09 Sun Microsystems, Inc. Scanning of evacuated objects in a generation managed by the train algorithm
US7788300B2 (en) * 2004-09-15 2010-08-31 Sap Ag Garbage collection for shared data entities
US7624395B2 (en) * 2004-09-23 2009-11-24 Sap Ag Thread-level resource usage measurement
US7895588B2 (en) * 2004-12-20 2011-02-22 Sap Ag System and method for detecting and certifying memory leaks within object-oriented applications
US9152531B2 (en) * 2005-02-18 2015-10-06 Green Hills Sofware, Inc. Post-compile instrumentation of object code for generating execution trace data
US7313661B1 (en) * 2005-03-18 2007-12-25 Sun Microsystems, Inc. Tool for identifying causes of memory leaks
US7581066B2 (en) * 2005-04-29 2009-08-25 Sap Ag Cache isolation model
US7810075B2 (en) * 2005-04-29 2010-10-05 Sap Ag Common trace files
US20070016893A1 (en) * 2005-07-14 2007-01-18 International Business Machines Corporation Tracking resource usage by applications
US7689558B2 (en) * 2005-09-09 2010-03-30 Sap Ag Application monitoring using profile points
US20070079307A1 (en) * 2005-09-30 2007-04-05 Puneet Dhawan Virtual machine based network carriers
US7877734B2 (en) * 2006-01-12 2011-01-25 International Business Machines Corporation Selective profiling of program code executing in a runtime environment
US7485062B2 (en) * 2006-02-27 2009-02-03 Chrysler Llc Hydraulic control system with variably regulated line pressure
JP2007279577A (en) * 2006-04-11 2007-10-25 Sony Corp Electronic equipment
US8601469B2 (en) * 2007-03-30 2013-12-03 Sap Ag Method and system for customizing allocation statistics
US7904493B2 (en) * 2007-03-30 2011-03-08 Sap Ag Method and system for object age detection in garbage collection heaps
US8356286B2 (en) * 2007-03-30 2013-01-15 Sap Ag Method and system for providing on-demand profiling infrastructure for profiling at virtual machines
US20080243970A1 (en) * 2007-03-30 2008-10-02 Sap Ag Method and system for providing loitering trace in virtual machines
US8522209B2 (en) * 2007-03-30 2013-08-27 Sap Ag Method and system for integrating profiling and debugging

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7086064B1 (en) * 2000-05-27 2006-08-01 International Business Machines Corporation Performance profiling tool
US20050240641A1 (en) * 2003-05-09 2005-10-27 Fujitsu Limited Method for predicting and avoiding danger in execution environment
US20070027942A1 (en) * 2005-07-27 2007-02-01 Trotter Martin J Memory leak detection
US20080209404A1 (en) * 2007-02-27 2008-08-28 International Business Machines Corporation Method and system for analyzing memory leaks occurring in java virtual machine data storage heaps

Cited By (43)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8336033B2 (en) 2007-03-30 2012-12-18 Sap Ag Method and system for generating a hierarchical tree representing stack traces
US20080243968A1 (en) * 2007-03-30 2008-10-02 Sap Ag Method and system for object age detection in garbage collection heaps
US20080244547A1 (en) * 2007-03-30 2008-10-02 Sap Ag Method and system for integrating profiling and debugging
US20080244531A1 (en) * 2007-03-30 2008-10-02 Sap Ag Method and system for generating a hierarchical tree representing stack traces
US20080244546A1 (en) * 2007-03-30 2008-10-02 Sap Ag Method and system for providing on-demand profiling infrastructure for profiling at virtual machines
US20100095280A1 (en) * 2007-03-30 2010-04-15 Ralf Schmelter Method and system for providing loitering trace in virtual machines
US7904493B2 (en) 2007-03-30 2011-03-08 Sap Ag Method and system for object age detection in garbage collection heaps
US8667471B2 (en) 2007-03-30 2014-03-04 Sap Ag Method and system for customizing profiling sessions
US8601469B2 (en) 2007-03-30 2013-12-03 Sap Ag Method and system for customizing allocation statistics
US8522209B2 (en) * 2007-03-30 2013-08-27 Sap Ag Method and system for integrating profiling and debugging
US20080243969A1 (en) * 2007-03-30 2008-10-02 Sap Ag Method and system for customizing allocation statistics
US7971010B2 (en) 2007-03-30 2011-06-28 Sap Ag Mechanism for performing loitering trace of objects that cause memory leaks in a post-garbage collection heap
US8356286B2 (en) 2007-03-30 2013-01-15 Sap Ag Method and system for providing on-demand profiling infrastructure for profiling at virtual machines
US20110138385A1 (en) * 2009-12-04 2011-06-09 Sap Ag Tracing values of method parameters
US20110138363A1 (en) * 2009-12-04 2011-06-09 Sap Ag Combining method parameter traces with other traces
US8527960B2 (en) 2009-12-04 2013-09-03 Sap Ag Combining method parameter traces with other traces
US8584098B2 (en) 2009-12-04 2013-11-12 Sap Ag Component statistics for application profiling
US20110138365A1 (en) * 2009-12-04 2011-06-09 Sap Ag Component statistics for application profiling
US20110138366A1 (en) * 2009-12-04 2011-06-09 Sap Ag Profiling Data Snapshots for Software Profilers
US8850403B2 (en) * 2009-12-04 2014-09-30 Sap Ag Profiling data snapshots for software profilers
US9129056B2 (en) 2009-12-04 2015-09-08 Sap Se Tracing values of method parameters
US20120159454A1 (en) * 2010-12-20 2012-06-21 Microsoft Corporation Probe insertion via background virtual machine
US10203974B2 (en) * 2010-12-20 2019-02-12 Microsoft Technology Licensing, Llc Probe insertion via background virtual machine
US10178031B2 (en) 2013-01-25 2019-01-08 Microsoft Technology Licensing, Llc Tracing with a workload distributor
US9658936B2 (en) 2013-02-12 2017-05-23 Microsoft Technology Licensing, Llc Optimization analysis using similar frequencies
US9767006B2 (en) 2013-02-12 2017-09-19 Microsoft Technology Licensing, Llc Deploying trace objectives using cost analyses
US9804949B2 (en) 2013-02-12 2017-10-31 Microsoft Technology Licensing, Llc Periodicity optimization in an automated tracing system
US20130227529A1 (en) * 2013-03-15 2013-08-29 Concurix Corporation Runtime Memory Settings Derived from Trace Data
US9436589B2 (en) 2013-03-15 2016-09-06 Microsoft Technology Licensing, Llc Increasing performance at runtime from trace data
US9665474B2 (en) 2013-03-15 2017-05-30 Microsoft Technology Licensing, Llc Relationships derived from trace data
US9864676B2 (en) 2013-03-15 2018-01-09 Microsoft Technology Licensing, Llc Bottleneck detector application programming interface
US9575874B2 (en) 2013-04-20 2017-02-21 Microsoft Technology Licensing, Llc Error list and bug report analysis for configuring an application tracer
US9864672B2 (en) 2013-09-04 2018-01-09 Microsoft Technology Licensing, Llc Module specific tracing in a shared module environment
US9772927B2 (en) 2013-11-13 2017-09-26 Microsoft Technology Licensing, Llc User interface for selecting tracing origins for aggregating classes of trace data
US20160011893A1 (en) * 2014-07-11 2016-01-14 Beeman C. Strong Managing generated trace data for a virtual machine
US9329884B2 (en) * 2014-07-11 2016-05-03 Intel Corporation Managing generated trace data for a virtual machine
US20160323160A1 (en) * 2015-04-29 2016-11-03 AppDynamics, Inc. Detection of node.js memory leaks
US9965375B2 (en) 2016-06-28 2018-05-08 Intel Corporation Virtualizing precise event based sampling
US10496522B2 (en) 2016-06-28 2019-12-03 Intel Corporation Virtualizing precise event based sampling
US11055203B2 (en) 2016-06-28 2021-07-06 Intel Corporation Virtualizing precise event based sampling
US11182272B2 (en) * 2018-04-17 2021-11-23 International Business Machines Corporation Application state monitoring
EP3819763A4 (en) * 2018-07-27 2021-08-25 Samsung Electronics Co., Ltd. Electronic device and operating method thereof
US11934853B2 (en) 2018-07-27 2024-03-19 Samsung Electronics Co., Ltd. Electronic device and operating method thereof

Also Published As

Publication number Publication date
US20100095280A1 (en) 2010-04-15
US7971010B2 (en) 2011-06-28

Similar Documents

Publication Publication Date Title
US7971010B2 (en) Mechanism for performing loitering trace of objects that cause memory leaks in a post-garbage collection heap
US8356286B2 (en) Method and system for providing on-demand profiling infrastructure for profiling at virtual machines
US8667471B2 (en) Method and system for customizing profiling sessions
US8601469B2 (en) Method and system for customizing allocation statistics
US8522209B2 (en) Method and system for integrating profiling and debugging
US7904493B2 (en) Method and system for object age detection in garbage collection heaps
US8336033B2 (en) Method and system for generating a hierarchical tree representing stack traces
US8229979B2 (en) Method and system for inspecting memory leaks
US7953772B2 (en) Method and system for inspecting memory leaks and analyzing contents of garbage collection files
KR101669630B1 (en) Conditional dynamic instrumentation of software in a specified transaction context
US7689558B2 (en) Application monitoring using profile points
US7313661B1 (en) Tool for identifying causes of memory leaks
US8832665B2 (en) Method and system for tracing individual transactions at the granularity level of method calls throughout distributed heterogeneous applications without source code modifications including the detection of outgoing requests
US8234631B2 (en) Method and system for tracing individual transactions at the granularity level of method calls throughout distributed heterogeneous applications without source code modifications
JP5705084B2 (en) 2-pass automatic application measurement
US7941789B2 (en) Common performance trace mechanism
US8793289B2 (en) Method and system for detecting memory leaks and copying garbage collection files
EP1172729A2 (en) Apparatus and method for cataloguing symbolic data for use in performance analysis of computer programs
US20060253507A1 (en) System and method for monitoring memory usage
JP2009516239A (en) General purpose multi-instance method and GUI detection system for tracking and monitoring computer applications
US7725771B2 (en) Method and system for providing enhanced memory error messages
EP2972881B1 (en) Diagnostics of state transitions
US8478738B2 (en) Object deallocation system and method
US20100262954A1 (en) Method for Locating Resource Leaks during Software Development
US20080091909A1 (en) Method and system to manage virtual machine memory

Legal Events

Date Code Title Description
AS Assignment

Owner name: SAP AG, GERMANY

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:SCHMELTER, RALF;WINTERGERST, MICHAEL;ZELLER, ARNO;AND OTHERS;REEL/FRAME:019187/0013;SIGNING DATES FROM 20070320 TO 20070322

STCB Information on status: application discontinuation

Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION