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

Ramasse-miettes générationnel et incémental gérant les cycles et les gros objets en utilisant des frames délimités

Ces dernières années, des recherches ont été menées sur plusieurs techniques reliées à la collection des déchets. Plusieurs découvertes centrales pour le ramassage de miettes par copie ont été réalisées. Cependant, des améliorations sont encore possibles. Dans ce mémoire, nous introduisons des nouvelles techniques et de nouveaux algorithmes pour améliorer le ramassage de miettes. En particulier, nous introduisons une technique utilisant des cadres délimités pour marquer et retracer les pointeurs racines. Cette technique permet un calcul efficace de l'ensemble des racines. Elle réutilise des concepts de deux techniques existantes, card marking et remembered sets, et utilise une configuration bidirectionelle des objets pour améliorer ces concepts en stabilisant le surplus de mémoire utilisée et en réduisant la charge de travail lors du parcours des pointeurs. Nous présentons aussi un algorithme pour marquer récursivement les objets rejoignables sans utiliser de pile (éliminant le gaspillage de mémoire habituel). Nous adaptons cet algorithme pour implémenter un ramasse-miettes copiant en profondeur et améliorer la localité du heap. Nous améliorons l'algorithme de collection des miettes older-first et sa version générationnelle en ajoutant une phase de marquage garantissant la collection de toutes les miettes, incluant les structures cycliques réparties sur plusieurs fenêtres. Finalement, nous introduisons une technique pour gérer les gros objets. Pour tester nos idées, nous avons conçu et implémenté, dans la machine virtuelle libre Java SableVM, un cadre de développement portable et extensible pour la collection des miettes. Dans ce cadre, nous avons implémenté des algorithmes de collection semi-space, older-first et generational. Nos expérimentations montrent que la technique du cadre délimité procure des performances compétitives pour plusieurs benchmarks. Elles montrent aussi que, pour la plupart des benchmarks, notre algorithme de parcours en profondeur améliore la localité et augmente ainsi la performance. Nos mesures de la performance générale montrent que, utilisant nos techniques, un ramasse-miettes peut délivrer une performance compétitive et surpasser celle des ramasses-miettes existants pour plusieurs benchmarks. ______________________________________________________________________________ MOTS-CLÉS DE L’AUTEUR : Ramasse-Miettes, Machine Virtuelle, Java, SableVM

Virtual Machine-Assisted Collaborative Junk Object Detection

Memory leak is unrecoverable software bug that causes performance degradation and re- liability issues to software applications. Although memory management systems exist in modern Object Oriented Language to reclaim unused memory store, memory leak can still happen, and continue to exhaust memory resource. In Java, where there is garbage col- lection for releasing unused objects, memory leaks manifest itself in the form of unused object retention. Since Java Programming language allocates objects on heap, the lifetime of an object deviates from the stack discipline, which can be a challenge in detecting Java memory leak. In this thesis, we propose a collaborative approach in detecting Java memory leaks through verifying Object Lifetime Specification at runtime. We designed a runtime verifier that leverages Java Virtual Machine technologies to monitor and extract annotated infor- mation from the user application, and use that information to verify against Java Virtual Machine events to detect unintentional object retention in the Java application under test. We implemented our runtime verifier with Maxine Virtual Machine, an open source, meta-circular virtual machine developed by Oracle Lab, and conducted experiments and DaCapo benchmark to evaluate its accuracy and performance efficiency. The results show that the runtime verification tool successfully identifies junk objects for different semantic cases proposed in this thesis with certain runtime overhead. Through the research and experimental results, we further make implications on how to improve the performance overhead associated with current design and implementation methods in detecting unused object retention, which in the long term constitute memory leak and performance bug

Profiling Initialisation Behaviour in Java

Freshly created objects are a blank slate: their mutable state and their constant properties must be initialised before they can be used. Programming languages like Java typically support object initialisation by providing constructor methods. This thesis examines the actual initialisation of objects in real-world programs to determine whether constructor methods support the initialisation that programmers actually perform. Determining which object initialisation techniques are most popular and how they can be identified will allow language designers to better understand the needs of programmers, and give insights that VM designers could use to optimise the performance of language implementations, reduce memory consumption, and improve garbage collection behaviour. Traditional profiling typically either focuses on timing, or uses sampling or heap snapshots to approximate whole program analysis. Classifying the behaviour of objects throughout their lifetime requires analysis of all program behaviour without approximation. This thesis presents two novel whole-program object profilers: one using purely class modification (#prof ), and a hybrid approach utilising class modification and JVM support (rprof ). #prof modifies programs using aspect-oriented programming tools to generate and aggregate data and examines objects that enter different collections to determine whether correlation exists between initialisation behaviour and the use of equality operators and collections. rprof confirms the results of an existing static analysis study of field initialisation using runtime analysis, and provides a novel study of object initialisation behaviour patterns

Using Class-Level Static Properties to Predict Object Lifetimes

Today, most modern programming languages such as C # or Java use an automatic memory management system also known as a Garbage Collector (GC). Over the course of program execution, new objects are allocated in memory, and some older objects become unreachable (die). In order for the program to keep running, it becomes necessary to free the memory of dead objects; this task is performed periodically by the GC. Research has shown that most objects die young and as a result, generational collectors have become very popular over the years. Yet, these algorithms are not good at handling long-lived objects. Typically, long-lived objects would first be allocated in the nursery space and be promoted (copied) to an older generation after surviving a garbage collection, hence wasting precious time. By allocating long-lived and immortal objects directly into infrequently or never collected regions, pretenuring can reduce garbage collection costs significantly. Current state of the art methodology to predict object lifetime involves off-line profiling combined with a simple, heuristic classification. Profiling is slow (can take days), requires gathering gigabytes of data that need to be analysed (can take hours), and needs to be repeated for every previously unseen program. This thesis explores the space of lifetime predictions and shows how object lifetimes can be predicted accurately and quickly using simple program characteristics gathered within minutes. Following an innovative methodology introduced in this thesis, object lifetime predictions are fed into a specifically modified Java virtual machine. Performance tests show gains in GC times of as much as 77% for the “SPEC jvm98” benchmarks, against a generational copying collector