A fast analysis for thread-local garbage collection with dynamic class loading

Long-running, heavily multi-threaded, Java server applications make stringent demands of garbage collector (GC) performance. Synchronisation of all application threads before garbage collection is a significant bottleneck for JVMs that use native threads. We present a new static analysis and a novel GC framework designed to address this issue by allowing independent collection of thread-local heaps. In contrast to previous work, our solution safely classifies objects even in the presence of dynamic class loading, requires neither write-barriers that may do unbounded work, nor synchronisation, nor locks during thread-local collections; our analysis is sufficiently fast to permit its integration into a high-performance, production-quality virtual machine

Garbage collection in distributed systems

PhD ThesisThe provision of system-wide heap storage has a number of advantages. However, when the technique is applied to distributed systems automatically recovering inaccessible variables becomes a serious problem. This thesis presents a survey of such garbage collection techniques but finds that no existing algorithm is entirely suitable. A new, general purpose algorithm is developed and presented which allows individual systems to garbage collect largely independently. The effects of these garbage collections are combined, using recursively structured control mechanisms, to achieve garbage collection of the entire heap with the minimum of overheads. Experimental results show that new algorithm recovers most inaccessible variables more quickly than a straightforward garbage collection, giving an improved memory utilisation

Garbage collection schemeoptimization in YAFFS

In this project we present an improved garbage collection scheme for YAFFS2 (Yet Another Flash File System). Our objective was to reduce the number of Aggressive Garbage Collections in Garbage Collection module of YAFFS2. We explore the various values of beta (ratio of number of erased chunks to number of free chunks) experimentally and try to find the optimal value of beta for which number of Aggressive Garbage Collections (Garbage Collection) is the least. Extensive tests were conducted by varying the value of beta (0< beta<1). A total of three tests were used to determine the number of Garbage Collections in different scenarios: a write test (clean write as well as overwrites), an erase test and a post erase test Garbage Collection monitoring test. A generic write test and an erase test were provided with YAFFS2 source code, which needed little modifications. The last test was developed by us as the situation demanded. Reduction in the number of Aggressive Garbage Collections reduces computational load on the processor, which in turn saves power, especially in battery powered devices

Liveness-Based Garbage Collection for Lazy Languages

We consider the problem of reducing the memory required to run lazy first-order functional programs. Our approach is to analyze programs for liveness of heap-allocated data. The result of the analysis is used to preserve only live data---a subset of reachable data---during garbage collection. The result is an increase in the garbage reclaimed and a reduction in the peak memory requirement of programs. While this technique has already been shown to yield benefits for eager first-order languages, the lack of a statically determinable execution order and the presence of closures pose new challenges for lazy languages. These require changes both in the liveness analysis itself and in the design of the garbage collector. To show the effectiveness of our method, we implemented a copying collector that uses the results of the liveness analysis to preserve live objects, both evaluated (i.e., in WHNF) and closures. Our experiments confirm that for programs running with a liveness-based garbage collector, there is a significant decrease in peak memory requirements. In addition, a sizable reduction in the number of collections ensures that in spite of using a more complex garbage collector, the execution times of programs running with liveness and reachability-based collectors remain comparable

Long Term Assessment of Object Strength in a Web Service as Managed by the Garbage Collection in Java Based Services

Garbage collection is proving to be an important feature that supports high-performance web services, especially those running data-intensive applications. Due to the use of the object-oriented paradigm, many applications have increasingly opted for the dynamic memory allocation method of assigning their objects in computer memory. During program execution, the application allocates its objects to a memory space called a heap and constantly references these objects within that memory space. With the passage of time, if the objects are not referenced, they become weak/dead to the extent that they can no longer be referenced by an application which allocated them. In such a scenario, the application is required to allocate new objects to a heap in order to continue performing its functions. And, there must be a garbage collection mechanism to remove the dead/weak (unreferenced) objects from the memory heap so that the memory space can be reclaimed and dynamically allocated to other application objects. Java as a Virtual machine, performs memory allocation and reclamation by itself thereby allowing the programmer to concentrate only on the functionality of the application. In other words, the developer is not concerned about how the memory will be managed during the program execution because that will be the duty of the Java language executing on Java Virtual machine. Therefore, for Java to effectively manage the computer memory, it uses five garbage collection mechanisms which will be explained in detail in the introduction section. Most of the garbage collections are triggered based on the objects’ lifetime predictions set by the developer of the garbage collection algorithms. None or very few consider the strength of the objects that are no longer referenced in the heap. For example, some objects may still be strong enough that they can be referenced by the application but they are collected anyway because they have reached their predicted age threshold. Garbage collection mechanisms also vary when used in a different framework other than the traditional (standalone) one. For example, garbage collection in distributed systems becomes more complicated as compared to the traditional garbage collection performed in standalone systems. Similarly, garbage collection in a web-service framework has slight differences as compared to the local/standalone systems due to the inclusion of web service technology elements. In this paper, the goal is to strive to determine the strength of objects that are no longer referenced by an application in a web service as managed by Java-based services; in relation to the performance of a web application

Incremental copying garbage collection for WAM-based Prolog systems

The design and implementation of an incremental copying heap garbage collector for WAM-based Prolog systems is presented. Its heap layout consists of a number of equal-sized blocks. Other changes to the standard WAM allow these blocks to be garbage collected independently. The independent collection of heap blocks forms the basis of an incremental collecting algorithm which employs copying without marking (contrary to the more frequently used mark&copy or mark&slide algorithms in the context of Prolog). Compared to standard semi-space copying collectors, this approach to heap garbage collection lowers in many cases the memory usage and reduces pause times. The algorithm also allows for a wide variety of garbage collection policies including generational ones. The algorithm is implemented and evaluated in the context of hProlog.Comment: 33 pages, 22 figures, 5 tables. To appear in Theory and Practice of Logic Programming (TPLP

Refined garbage collection for open distributed systems with multicapabilities

Capabilities can provide information not only on a particular object, but also on which methods of the object an agent is permitted to invoke. Specific information about an agent‘s ‘knowledge’ is potentially very useful and can be manipulated in a variety of ways. This paper focuses on the LINDA coordination model of open distributed systems. One limited resource is memory, and garbage collection has already been proposed for the standard LINDA with multiple tuple-spaces (TSs) to avoid memory exhaustion. The implementation, however, was restricted to garbage collection of TSs. Taking into account the need for garbage collection not only for TSs, but also for tuples, this paper demonstrates how the garbage collection mechanism can be extended to handle unusable tuples, with the introduction of multicapabilities, which generalise capabilities to collections of objects

The economics of garbage collection

This paper argues that economic theory can improve our understanding of memory management. We introduce the allocation curve, as an analogue of the demand curve from microeconomics. An allocation curve for a program characterises how the amount of garbage collection activity required during its execution varies in relation to the heap size associated with that program. The standard treatment of microeconomic demand curves (shifts and elasticity) can be applied directly and intuitively to our new allocation curves. As an application of this new theory, we show how allocation elasticity can be used to control the heap growth rate for variable sized heaps in Jikes RVM