Patent Description:
Many programming languages include garbage collection. In some programming languages, garbage collection can be included as part of the language specification such as C#, Java, D, Go, and many scripting languages. Additionally, some languages such as C and C++ are designed for manual memory management but include garbage-collected implementations. Further, some languages such as C++ for Common Language Infrastructure (C++/CLI) allow for both garbage collection and manual memory management to co-exist in the same application by using separate memory segments for collected and manually managed objects. Garbage collection is often integrated into the language compiler and the runtime system.

<CIT> relates to a method that includes selectively controlling, at a computing device having a memory, initiation of a full garbage collection operation based on a total resource usage metric and a managed object metric. The managed object metric is based on objects managed by a runtime application.

<CIT> relates to systems and methods that are provided to automatically analyze performance of an automatic memory management system. One example involves automatically gathering, using at least one processor of the server, garbage collection information associated with the garbage collection process and storing the garbage collection information in a garbage collection output file of a file system. The garbage collection output file may be analyzed to identify a plurality of flags associated with a performance of the server system that does not meet one or more performance thresholds. In certain examples, a first flag of the plurality of flags is associated with a first portion of the garbage collection information, and a second flag of the plurality of flags is associated with a second portion of the garbage collection information that is different from the first portion of the garbage collection information.

<CIT> relates to a method that is provided for garbage collection in a heap of an application server that uses automated garbage collection. The method comprises gathering information about a plurality of garbage collection events; identifying a correlation between garbage collection activity and a volume of applications transactions in the application server; based on the correlation, forecasting at least one of a future utilization of heap memory and a future garbage collection activity based on a projected future transaction volume; and tuning the heap based on the forecast.

It is the object of the present invention to provide different types of garbage collection.

This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Description.

Systems and methods for different garbage collections based on one of multiple heap size stages are disclosed. In one example, the multiple garbage collections are concurrent garbage collections provided in a dedicated thread concurrently running in the computing device with a mutator thread. A heap size stage, from multiple heap size stages including a heap size growth stage and a heap size stable stage, is determined from a free space amount subsequent a garbage collection. A heap stable garbage collection is applied in response to the heap size stage being the heap size stable stage. A heap growth garbage collection is applied in response to the heap size stage being the heap size growth stage. In one example, the heap stable and heap growth garbage collections can include different garbage collection goals, tuning parameters, mechanisms, or suitable other distinctions.

The accompanying drawings are included to provide a further understanding of embodiments and are incorporated in and constitute a part of this disclosure. Other embodiments and many of the intended advantages of embodiments will be readily appreciated, as they become better understood by reference to the following description.

In the following Description, reference is made to the accompanying drawings, which form a part hereof, and in which is shown by way of illustration specific embodiments in which the invention may be practiced. It is to be understood that other embodiments may be utilized and structural or logical changes may be made without departing from the scope of the present invention. The following description, therefore, is not to be taken in a limiting sense. It is to be understood that features of the various exemplary embodiments described herein may be combined with each other, unless specifically noted otherwise.

<FIG> illustrates an exemplary computer system that can be employed in an operating environment and used to host or run a computer application included on one or more computer readable storage mediums storing computer executable instructions for controlling the computer system, such as a computing device, to perform a process. An example of a computer-implemented process includes a concurrent garbage collection that can be stored in a computer memory and executed with a processor to apply one of multiple garbage collection parameters corresponding with multiple heap size stages.

The exemplary computer system includes a computing device, such as computing device <NUM>. In a basic hardware configuration, computing device <NUM> typically includes a processor system having one or more processing units, i.e., processors <NUM>, and memory <NUM>. By way of example, the processing units may include two or more processing cores on a chip or two or more processor chips. In some examples, the computing device can also have one or more additional processing or specialized processors (not shown), such as a graphics processor for general-purpose computing on graphics processor units, to perform processing functions offloaded from the processor <NUM>. The memory <NUM> may be arranged in a hierarchy and may include one or more levels of cache. Depending on the configuration and type of computing device, memory <NUM> may be volatile (such as random access memory (RAM)), non-volatile (such as read only memory (ROM), flash memory, etc.), or some combination of the two. The computing device <NUM> can take one or more of several forms. Such forms include a tablet, a personal computer, a workstation, a server, a handheld device, a consumer electronic device (such as a video game console or a digital video recorder), or other, and can be a stand-alone device or configured as part of a computer network.

Computing device <NUM> can also have additional features or functionality. For example, computing device <NUM> may also include additional storage. Such storage may be removable and/or non-removable and can include magnetic or optical disks, solid-state memory, or flash storage devices such as removable storage <NUM> and non-removable storage <NUM>. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any suitable method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Memory <NUM>, removable storage <NUM> and non-removable storage <NUM> are all examples of computer storage media. Computer storage media includes RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile discs (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, universal serial bus (USB) flash drive, flash memory card, or other flash storage devices, or any other storage medium that can be used to store the desired information and that can be accessed by computing device <NUM>. Accordingly, a propagating signal by itself does not qualify as storage media. Any such computer storage media may be part of computing device <NUM>.

Computing device <NUM> often includes one or more input and/or output connections, such as USB connections, display ports, proprietary connections, and others to connect to various devices to provide inputs and outputs to the computing device. Input devices <NUM> may include devices such as keyboard, pointing device (e.g., mouse, track pad), stylus, voice input device, touch input device (e.g., touchscreen), or other. Output devices <NUM> may include devices such as a display, speakers, printer, or the like.

Computing device <NUM> often includes one or more communication connections <NUM> that allow computing device <NUM> to communicate with other computers/applications <NUM>. Example communication connections can include an Ethernet interface, a wireless interface, a bus interface, a storage area network interface, and a proprietary interface. The communication connections can be used to couple the computing device <NUM> to a computer network, which can be classified according to a wide variety of characteristics such as topology, connection method, and scale. A network is a collection of computing devices and possibly other devices interconnected by communications channels that facilitate communications and allows sharing of resources and information among interconnected devices. Examples of computer networks include a local area network, a wide area network, the Internet, or other network.

Computing device <NUM> can be configured to run an operating system software program and one or more computer applications, which make up a system platform. A computer application configured to execute on the computing device <NUM> includes at least one process (or task), which is an executing program. Each process provides the resources to execute the program. One or more threads run in the context of the process. A thread is the basic unit to which an operating system allocates time in the processor <NUM>. The thread is the entity within a process that can be scheduled for execution. Threads of a process can share its virtual address space and system resources. Each thread can include exception handlers, a scheduling priority, thread local storage, a thread identifier, and a thread context, or thread state, until the thread is scheduled. A thread context includes the thread's set of machine registers, the kernel stack, a thread environmental block, and a user stack in the address space of the process corresponding with the thread. Threads can communicate with each other during processing through techniques such as message passing.

An operation may execute in a thread separate from the main application thread. When an application calls methods to perform an operation, the application can continue executing on its thread while the method performs its task. Concurrent programming for shared-memory multiprocessors can include the ability for multiple threads to access the same data. The shared-memory model is the most commonly deployed method of multithread communication. Multiple threads execute on multiple processors, multiple processor cores, multiple logical nodes in a single processor core, and/or other classes of parallelism that are attached to a memory shared between the processors.

The present disclosure relates generally to garbage collectors and methods to provide garbage collection, such as concurrent garbage collection or concurrent and non-compacting garbage collection, used with programming languages or runtime systems in a data processing system such as computing device <NUM>. Aspects of the present disclosure may be embodied as a system, method or computer program product. Accordingly, aspects of the present disclosure may take the form of entirely hardware, entirely software, including firmware, resident software, micro-code, or a combination of software and hardware aspects that may all generally be referred to as a system. Furthermore, aspects of the present disclosure may take the form of a computer program product including one or more computer readable medium or media having computer readable program instruction for causing a processor to carry out the aspects of the disclosure.

<FIG> illustrates features of an example software framework <NUM>, which can be implemented on computing device <NUM>. The framework <NUM> can be used with developer-written software applications created in one or more framework-compatible languages for one or more platforms. Example framework <NUM> includes a class library <NUM> having a runtime library and base class library and an application engine such as a runtime system <NUM> or virtual machine. In one example, the class library <NUM> includes a set of classes organized by namespace to define features available in a framework-compatible programming language. Software applications written in a framework-compatible language as source code are compiled into a platform-neutral language, or bytecode, that can be executed in a platform-specific virtual machine installed on the platform, such as computing device <NUM>. The runtime system <NUM> compiles the bytecode into machine code that is executed on the platform. The runtime system <NUM> can provides additional services including memory management, type safety, exception handling, garbage collection, security and thread management. Upon execution of the developer-written program, a platform-specific just-in-time compiler <NUM> of the runtime system <NUM> translates the byte code into machine code. The compiler <NUM> can provide a combination of ahead-of-time compilation and interpretation, and the runtime system <NUM> can handle late-bound data types and enforce security guarantees.

Class library <NUM> of the example can include one or more class or classes <NUM> implemented in accordance with the methods disclosed. In general, a class <NUM> is an extensible program-code-template, or blueprint, for creating objects, providing initial values for state, and implementations of behavior. The class is a construct that enables a developer to create custom types by grouping together variables of other types, methods and events. Class or classes <NUM> may include class variables, instance variables, local variables, parameters, user-defined methods, inherited states and behaviors, and interfaces. The variable can remain in memory <NUM> until all references go out of scope. At that time, the runtime system <NUM> via garbage collector <NUM> can mark the variable as eligible for garbage collection.

Garbage collector <NUM> automatically manages the allocation and release of memory for the developer-written application. The runtime system <NUM> allocates a segment of memory <NUM> to store and manage objects called the managed heap. (In one example, the managed heap is distinguishable from a native heap in the operating system. For the purposes of this disclosure, "heap" refers to the managed heap. ) For each new object, the runtime system <NUM> allocates memory for the object from the managed heap. In one example, there can be a managed heap for each managed process, and threads in the process allocate memory for objects on the same heap. In another example, the heap can be an accumulation of a large object heap, such as a heap that includes objects over a selected threshold in size, and a small object heap.

As address space becomes occupied on the managed heap, the garbage collector <NUM> eventually frees some memory. The garbage collector <NUM> includes an optimizing engine <NUM> to determine the preferred time or occasion to perform a collection, which can be based upon a dynamically tunable parameter from a previous garbage collection. The garbage collector <NUM> checks for objects in the managed heap that are no longer being used by the application and performs the operations to reclaim the memory. Garbage collection can occur when the system has low physical memory or if the memory used by allocated objects on the managed heap surpasses an acceptable threshold. In one example, the threshold can be dynamically adjusted as based on a previous garbage collection.

In one example, the heap can be a generational heap. The heap can be organized into multiple generations to tackle long-lived and short-lived objects. Garbage collection primarily occurs with the reclamation of short-lived objects that typically occupy a small part of the heap. One example includes three generations of objects on the heap including a generation <NUM>, generation <NUM>, and generation <NUM>. Generation <NUM> is the youngest generation and contains short-lived objects such as a temporary variable. Garbage collection occurs most frequently in this generation. In one example, newly allocated objects form a new generation of objects and are implicitly generation <NUM> collections, unless they are large objects, in which case they go on the large object heap in a generation <NUM> collection. Many objects are reclaimed for garbage collection in generation <NUM> and do not survive to the next generation. Generation <NUM> includes short-lived objects and can serves as a buffer between short-lived objects and long-lived objects. Some example garbage collectors do not include a generation <NUM> heap and only include heaps for short-lived and long-lived objects. Additionally, one or more generations of short-lived objects can be known as ephemeral generations. Generation <NUM> includes long-lived objects. An example of a long-lived object is an object in a server application that contains static data that is live for the duration of the process. Garbage collections occur on specific generations as conditions warrant. Collecting a generation means collecting objects in that generation and all its younger generations. A generation <NUM> garbage collection is typically a full garbage collection because it reclaims all objects in all generations of the managed heap.

Objects that are not reclaimed in a garbage collection are known as survivors and are promoted to the next generation. For example, objects that survive a generation <NUM> garbage collection are promoted to generation <NUM>, objects that survive a generation <NUM> garbage collection are promoted to generation <NUM>, and objects that survive a generation <NUM> garbage collection remain in generation <NUM>.

Before a garbage collection starts, or is triggered, the managed threads can be suspended except for the thread that triggered the garbage collection. The garbage collector can determine whether an object is live via information such as stack variables provided by a just-in-time compiler and stack walker, handles that point to managed objects and that can be allocated by user code or by the runtime, and from static objects in application domains that could be referencing other objects. Each application domain keeps track of its static objects. In one example, garbage collection can occur in a set of phases including marking phase that finds and creates a list of all live objects, a relocating phase that updates the references to the objects that will be compacted, and a reclamation phase that reclaims the space occupied by the dead objects. In some examples, the reclamation phase can include compacting the surviving objects in which objects that have survived a garbage collection are moved toward the older end of the segment. Some example garbage collectors, such as garbage collector <NUM>, do not include a compacting feature. Such non-compacting garbage collectors are prone to heap size growth.

Concurrent garbage collection is a form of garbage collection that enables threads to run concurrently with a dedicated thread that performs the garbage collection for at least some of the duration of the dedicated thread that performs the garbage collection. For example, a concurrent garbage collection can run in the dedicated thread while one or more mutator threads are running, i.e., a thread that mutates the managed heap. Concurrent garbage collection can be performed on generational and non-generational heaps. In one example, concurrent garbage collection affects garbage collections for long-lived objects such as generation <NUM>. For example, garbage collection in generation <NUM> and generation <NUM> are performed non-concurrently because they can be completed quickly and not noticeably affect performance.

Concurrent garbage collection can enable some software applications to be more responsive by eliminating or reducing pauses of a software application for a garbage collection. Managed threads can continue to run for at least some of the time while the concurrent garbage collection thread is running. This can eliminate pauses or result in shorter pauses during a garbage collection. Concurrent garbage collection can trade some processor performance and memory for shorter pauses during garbage collection.

Concurrent garbage collector <NUM> attempts to improve performance via preferred triggering of the collection with optimizing engine <NUM>. Concurrent garbage collector in general presents a performance overhead, and running too often or when not preferred can adversely affects performance of the concurrently running programs. If the collection is triggered to early, the free memory space has not been efficiently used and performance of the concurrently running programs is adversely affected. If the collection is triggered too late and the managed heap runs out of memory space, the generational size of the managed heap may need to be extended which is adversely affects the performance of non-compacting concurrent garbage collectors. Performance of compacting concurrent garbage collectors can suffer as well as mutator threads may have to wait for more memory space to become available.

Concurrent garbage collection is affected by unpredictable factors such as operating system thread scheduling and sudden changes in application behavior as well as other factors. Due to the non-deterministic nature of these factors, predicting the pace of building free memory space and consuming the free memory space is difficult. Thus, triggering concurrent garbage collection from a statically preconfigured amount of free memory space left available in the managed heap is generally inefficient and can run the risk of either running out of free space, and having to acquire more memory from the operating system, or not utilizing the free space built up from a previous garbage collection.

In some circumstances, however, processes are better served or improve performance with a growing heap size. In these circumstances, growing a heap size is justified and even preferred. Examples of preferred heap size growth can include application start up, when memory usage continues to grow, or if an application memory suddenly grows, when an application allocates lots of long-lived data. In these circumstances, application performance or overhead can be adversely affected if the same garbage collections are applied during stable heap-size stages and during heap-size growth stages.

<FIG> illustrates an example method <NUM> for use with garbage collector <NUM>, such as in optimizing engine <NUM>, of software framework <NUM> to provide multiple concurrent garbage collections for multiple stages of heap size. In one example, the garbage collections are concurrent, non-compacting garbage collections provided in a dedicated thread concurrently running in the computing device <NUM> with a mutator thread but of differing natures.

A heap size stage, from multiple heap size stages including a heap size growth stage and a heap size stable stage, is determined at <NUM> from a free space amount at the end of a garbage collection. In one example, the free space amount is a free space ratio of free memory space to the heap size at the completion of garbage collection, i.e., when the free memory space is at the maximum. A heap stable garbage collection is applied in response to the heap size stage being the heap size stable stage at <NUM>. A heap growth garbage collection is applied in response to the heap size stage being the heap size growth stage at <NUM>. In one example, the heap stable and heap growth garbage collections include different garbage collection tuning parameters, mechanisms, or suitable other distinctions. The heap stable garbage collection is attempting to use more of the heap size than the heap growth stage garbage collection.

<FIG> illustrates an example method <NUM> for use with the garbage collector <NUM>, such as in optimizing engine <NUM>, of software framework <NUM> to provide a heap stable stage garbage collection during the heap size stable stage at <NUM>. In one example, the heap stable garbage collection is a concurrent garbage collection provided on a dedicated thread concurrently running in the computing device <NUM> with a mutator thread at <NUM>. The concurrent garbage collection is triggered based on a dynamically tunable parameter from a previous garbage collection at <NUM>. In this example, the dynamically tunable parameter of <NUM> is based on a closed-loop feedback of previous garbage collections.

In one example, the optimizing engine <NUM> schedules the heap stable garbage collection at <NUM> on a trigger based on closed-loop feedback. The input in one example is the error based on a goal that indicates an amount of free memory space when the free memory space is at the smallest such as right before the heap stable garbage collection rebuilds free space, and the output can include an amount of memory that will be consumed in the free space by other generation allocations before the next garbage collection is triggered. The output can be extended to any other kinds of factors that will denote the triggering of the next concurrent heap stable garbage collection, such as the number of younger generation garbage collections that will be performed before the next concurrent garbage collection is triggered. In one example, an amount of free space is a ratio of free memory space to heap size. In another example, the amount of free space can be a number of bytes of free memory.

To initialize the heap stable garbage collection at <NUM>, the optimizing engine can apply parameters initially configured by the runtime system <NUM>. In one example, a user may further configure or selectively adjust the parameters. For instance, a free memory space goal amount of free space can be selected to include <NUM>% of the heap. The initial trigger can include the goal amount plus an additional free space amount, such as <NUM>%. For example, the initial garbage collection once the stable heap size stage is determined at <NUM> can be triggered when <NUM>% of the heap is free memory space. An error amount can be set to include the difference between the actual amount of free space at trigger and the goal amount of free space.

<FIG> illustrates an example method <NUM> for use with the garbage collector <NUM>, such as in optimizing engine <NUM>, of software framework <NUM> to apply the dynamically tunable parameter for a heap stable garbage collection at <NUM> using the closed-loop feedback. The current trigger, or new output can be based on one or more of plurality of feedback factors including the error and the previous output based on closed-loop feedback. For example, a first feedback factor can include accounting for present values of error from a set point, or goal at <NUM>. If the error is large and positive, the output will also be large and positive. The second feedback factor can include accounting for past values of the error at <NUM>. If the current output is not sufficiently strong, for example, the second feedback factor of error will accumulate over time, and the optimizing engine <NUM> will respond by applying a stronger action. The third feedback factor can account for possible future trends of the error and can be based on a current rate of change at <NUM>. In one example, the third feedback factor at <NUM> can be optionally or selectively applied.

In one example, first feedback factor at <NUM> includes a constant multiplied by the error amount and the second factor at <NUM> is the previous output. For example, new_output = previous_output * (<NUM> + constant * error_amount). The new output becomes the previous_output in subsequent garbage collections, and the error_amount is calculated as the difference between the input and the goal.

In one example, the third feedback factor at <NUM> can include the smaller of the adjustment to the trigger and a preselected amount. In one example, the adjustment can be expressed (new output - previous_output)/ previous_output.

Thus, if the adjustment is greater than a preselected amount, such as X%, the third feedback factor applied is X%. In one example, the third feedback factor is not applied on each trigger. For example, the third feedback factor can be applied if the new_output crosses the goal amount. In another example of a third feedback factor applied on a crossing of the goal amount, the amount of the adjustment can be set to half of the previous_output and the new_output. In this example, an adjustment_amount added to the new output can be set to (new output + previous_output)/ <NUM>.

The heap growth garbage collection at <NUM>, such as the garbage collection at the heap size growth stage, can also be a concurrent garbage collection as described above with reference to method <NUM> and provided in a dedicated thread concurrently running in the computing device <NUM> with a mutator thread at <NUM>. When the application starts up, it goes through the heap growth stage. During the life of the application, it may stay at heap stable stage or at various times go through the heap growth stage again, i.e., when the total live data used by the application has increased. A garbage collection goal of a different nature is set for the heap growth stage than the heap stable stage. Instead of trying to use the free space that is built up efficiently, the emphasis is to control how big to allow the heap to grow to be. The heap growth stage goal is based on when the free space is at its largest. In some examples this means when the concurrent garbage collection just finished building the free space. At this time the free space is checked and compared with the heap growth goal. If the free space amount or ratio is higher than that goal, it is treated as the heap stable stage; otherwise it is treated as we are still in heap growth stage because the free space is still small. The output of the heap growth stage may, in some examples, be calculated via the same fashion as the heap stable stage. In another example, the input of the heap growth garbage collection is not adjusted based on a closed-loop feedback control process as in the heap stable garbage collection. In still another example, neither the heap stable garbage collection at <NUM> nor the heap growth garbage collection at <NUM> are based on a closed-loop feedback control process of the examples set forth in methods <NUM>, <NUM>.

<FIG> illustrates an example method <NUM> for use with the garbage collector <NUM>, such as in optimizing engine <NUM>, of software framework <NUM> to determine heap size stage at <NUM>, from multiple heap size stages including the heap size growth stage and the heap size stable stage, using a free space amount at the end of a garbage collection.

As an optimization, instead of a heap growth goal, we implement a range around that goal to decide which stage we are in. an upper threshold and a lower threshold can be defined at <NUM>. The upper threshold and lower threshold are related to the heap growth goal. For example, the upper threshold can be defined as the heap growth free space goal plus a selected amount, or small delta. The lower threshold can be defined as the heap growth free space goal minus another selected amount, or small delta. In one example, the upper threshold to defined at <NUM> is greater than the lower threshold so the garbage collection does not change when the free space amount is between thresholds. Additionally, an initial stage can be defined at <NUM>, such as the optimizer <NUM> set to begin the process <NUM> determined to be the heap size growth stage to apply the heap growth garbage collection, or garbage collection during the heap size growth stage at <NUM>. /* initialize the garbage collector stage to heap growth*/ var bool stableStage = false;.

If the free space ratio at its largest is greater than the upper threshold at <NUM>, then the heap size stage is determined to be the heap size stable stage at <NUM>. If, instead, the free space ratio at its largest (free space on heap to total size of heap) is less than the lower threshold at <NUM>, then the heap size stage is determined to be the heap size growth stage at <NUM>. If the free space ratio at its largest is neither greater than the upper threshold at <NUM> nor less than the lower threshold at <NUM>, then the heap size stage from the previous garbage collection is maintained at <NUM>. if (currentFreeSpaceRatio > upperThreshold) {
stable Stage = true
}
else if (currentFreeSpaceRatio < lowerThreshold {
stable Stage = false }
/* otherwise don't change values of stableStage */.

Depending on whether the heap size stage is the heap size growth stage or the heap size stable stage at <NUM>, the corresponding garbage collection is applied at <NUM>, <NUM>. For example, the heap stable garbage collection is applied at <NUM> in response to the heap size stage being the heap size stable stage at <NUM>, and the heap growth garbage collection is applied at <NUM> in response to the heap size stage being the heap size growth stage at <NUM>. Subsequent the garbage collection at <NUM> or <NUM>, such as at the completion of the garbage collection when free space is likely at the highest amount, the free space ratio is measured at <NUM>. The free space ratio measured at <NUM> is applied to repeat the process for the next garbage collection at <NUM>. if (stableStage) {
// use tuning, goals for stable stage garbage collection
}
else {
/* use a different tuning and goals or mechanism for growth stage garbage collection */ }.

Claim 1:
A method of garbage collection in a computing device, comprising:
determining (<NUM>), based on an amount of free space at the completion of a previous garbage collection, a heap size stage from a plurality of heap size stages including a heap size growth stage and a heap size stable stage, wherein the heap size stable stage and the heap size growth stage have different garbage collection goals;
applying (<NUM>) a heap stable garbage collection in response to the heap size stage determined to be the heap size stable stage to use the free space efficiently according to a heap stable goal; and
applying (<NUM>) a heap growth garbage collection in response to the heap size stage determined to be the heap size growth stage to allow the heap to grow according to a heap growth goal.