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Portillo Dominguez, Andres Omar
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Portillo Dominguez, Andres Omar
Official Name
Portillo Dominguez, Andres Omar
Research Output
Now showing 1 - 7 of 7
- PublicationImproving the Testing of Java Garbage Collection Through an Efficient Benchmark GenerationGarbage Collection (GC) is a core feature of multiple modern technologies (e.g., Java, Android). On one hand, it offers significant software engineering benefits over explicitly memory management, like preventing most types of memory leaks. On the other hand, GC is a known cause of performance degradation. However, it is considerably challenging to understand its exact impact on the overall application performance. This is because the non-deterministic nature of GC makes very complex to properly model it and evaluate its performance impacts. To help tackling these problems, we present an engine to generate realistic GC benchmarks by enabling to effectively capture the GC/memory behaviours experienced by real-world Java applications. We also demonstrate, through a comprehensive experimental evaluation, how such benchmarks can be useful to strengthen the evaluation of GC-related advancements.
526 - PublicationAdaptive GC-aware load balancing strategy for high-assurance Java distributed systemsHigh-Assurance applications usually require achieving fast response time and high throughput on a constant basis. To fulfil these stringent quality of service requirements, these applications are commonly deployed in clustered instances. However, how to effectively manage these clusters has become a new challenge. A common approach is to deploy a front-end load balancer to optimise the workload distribution among the clustered applications. Thus, researchers have been studying how to improve the effectiveness of a load balancer. Our previous work presented a novel load balancing strategy which improves the performance of a distributed Java system by avoiding the performance impacts of Major Garbage Collection, which is a common cause of performance degradation in Java applications. However, as that strategy used a static configuration, it could only improve the performance of a system if the strategy was configured with domain expert knowledge. This paper extends our previous work by presenting an adaptive GC-aware load balancing strategy which self-configures according to the GC characteristics of the application. Our results have shown that this adaptive strategy can achieve higher throughput and lower response time, compared to the round-robin load balancing, while also avoiding the burden of manual tuning.
432Scopus© Citations 9 - PublicationTowards an Efficient Benchmark Generation Engine for Garbage CollectionGarbage Collection (GC) is a key feature of many modern programming technologies. It offers significant software engineering benefits over explicitly memory management. Nonetheless, it is also a major cause of performance degradation. As the rate of adoption of GC-related technologies continues to grow, it is highly relevant to understand its performance impact. However, this is challenging due to the non-deterministic nature of GC. To tackle this problem, we present an engine (HERMES) to create realistic GC benchmarks by effectively capturing the GC/memory behaviours exhibited by real-world Java applications. Our experiments prove how HERMES can be useful to strengthen the evaluation of GC-related advancements. This is achieved by broadening the number and diversity of the test scenarios, as well as reducing the time invested in testing.
364Scopus© Citations 1 - PublicationLoad balancing of Java applications by forecasting garbage collections(IEEE, 2014-06-27)
; ; ; ; Modern computer applications, especially at enterprise-level, are commonly deployed with a big number of clustered instances to achieve a higher system performance, in which case single machine based solutions are less cost-effective. However, how to effectively manage these clustered applications has become a new challenge. A common approach is to deploy a front-end load balancer to optimise the workload distribution between each clustered application. Since then, many research efforts have been carried out to study effective load balancing algorithms which can control the workload based on various resource usages such as CPU and memory. The aim of this paper is to propose a new load balancing approach to improve the overall distributed system performance by avoiding potential performance impacts caused by Major Java Garbage Collection. The experimental results have shown that the proposed load balancing algorithm can achieve a significant higher throughput and lower response time compared to the round-robin approach. In addition, the proposed solution only has a small overhead introduced to the distributed system, where unused resources are available to enable other load balancing algorithms together to achieve a better system performance.420Scopus© Citations 6 - PublicationTRINI: an adaptive load balancing strategy based on garbage collection for clustered Java systems(Wiley, 2016-12)
; ; ; ; Nowadays, clustered environments are commonly used in high-performance computing and enterprise-level applications to achieve faster response time and higher throughput than single machine environments. Nevertheless, how to effectively manage the workloads in these clusters has become a new challenge. As a load balancer is typically used to distribute the workload among the cluster's nodes, multiple research efforts have concentrated on enhancing the capabilities of load balancers. Our previous work presented a novel adaptive load balancing strategy (TRINI) that improves the performance of a clustered Java system by avoiding the performance impacts of major garbage collection, which is an important cause of performance degradation in Java. The aim of this paper is to strengthen the validation of TRINI by extending its experimental evaluation in terms of generality, scalability and reliability. Our results have shown that TRINI can achieve significant performance improvements, as well as a consistent behaviour, when it is applied to a set of commonly used load balancing algorithms, demonstrating its generality. TRINI also proved to be scalable across different cluster sizes, as its performance improvements did not noticeably degrade when increasing the cluster size. Finally, TRINI exhibited reliable behaviour over extended time periods, introducing only a small overhead to the cluster in such conditions. These results offer practitioners a valuable reference regarding the benefits that a load balancing strategy, based on garbage collection, can bring to a clustered Java system.433Scopus© Citations 11 - PublicationPerformance optimisation of clustered java systems(University College Dublin. School of Computer Science , 2016)
; ; Nowadays, clustered environments are commonly used in enterprise-levelapplications to achieve faster response time and higher throughput thansingle machine environments. However, this shift from a monolithic architecture to a distributed one has augmented the complexity of these applications, considerably complicating all activities related to the performance optimisation of such clustered systems. Therefore, automatic techniques are needed to facilitate these performance-related activities, which otherwise would be highly error-prone and time-consuming. This thesis contributes to the area of performance optimisation of clustered systems in Java (a predominant technology at enterprise-level), especially aiming for large-scale environments. This thesis proposes two techniques to solve the problems of efficiently identifying workload-dependent performance issues and efficiently avoiding the performance impacts of major garbage collection, two problems that a typical clustered Java system would likely suffer in large-scale environments. In particular, this thesis introduces an adaptive framework to automate the usage of performance diagnosis tools in the performance testing of clustered systems. The aim is to ease the identification of performance issues by decreasing the effort and expertise needed to effectively use such tools. Additionally, an adaptive GC-aware load balancing strategy is introduced, which leverages on major garbage collection forecasts to decide on the best way to balance the workload across the available nodes. The aim is to improve the performance of a clustered system by avoiding the impacts in the cluster's performance due to the major garbage collection occurring at the individual nodes. Experimental results of applying these techniques to a set of real-life applications are presented, showing the benefits that the techniques bring to a clustered Java system.320 - PublicationImproving the Testing of Clustered Systems Through the Effective Usage of Java Benchmarks(2017-10-27)
; Nowadays, cluster computing has become a cost-effective and powerful solution for enterprise-level applications. Nevertheless, the usage of this architecture model also increases the complexity of the applications, complicating all activities related to performance optimisation. Thus, many research works have pursued to develop advancements for improving the performance of clusters. Comprehensively evaluating such advancements is key to understand the conditions under which they can be more useful. However, the creation of an appropriate test environment, that is, one which offers different application behaviours (so that the obtained conclusions can be better generalised) is typically an effort-intensive task. To help tackle this problem, this paper presents a tool that helps to decrease the effort and expertise needed to build useful test environments to perform more robust cluster testing. This is achieved by enabling the effective usage of Java Benchmarks to easily create clustered test environments; hence, diversifying the application behaviours that can be evaluated. We also present the results of a practical validation of the proposed tool, where it has been successfully applied to the evaluation of two cluster-related advancements.476