Adaptive GC-Aware Load Balancing Strategy for High-Assurance Java Distributed Systems

Portillo-Dominguez, A. Omar; Wang, Miao; Murphy, John; Magoni, Damien

doi:10.1109/hase.2015.19

Cited by 13 publications

(18 citation statements)

References 22 publications

(30 reference statements)

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“…Also, in those experiments, no single FWS achieved the lowest forecast error (FE) in all the cases, showing that there is no 'best-fit-for-all' FWS. Meanwhile, the results of our work at [13] showed that the MaGA algorithm tends to benefit from having more historical data available. However, this growth is usually not monotonic.…”

Section: Migc-cv Program Familiesmentioning

confidence: 97%

“…Based on the observed behaviours, three M i G C C V program families were experimentally identified : low ( M i G C C V ≤0.1), medium (0.1< M i G C C V <1.0), and high (

MiG C_{CV} \geq

1.0). For each family, a FWS trending function was derived, focusing on those MaGCs that benefit from using the increments in MiGC history (while leaving the outliers out of the trend).…”

Section: Trini: An Adaptive Garbage Collection‐aware Load Balancing Smentioning

confidence: 99%

“…Based on the observed behaviours, three M iGC C V program families were experimentally identified [13]:…”

Section: Migc-cv Program Familiesmentioning

confidence: 99%

“…This strategy can therefore help to avoid impacts in the cluster's performance because of MaGC events. In our previous work , we presented TRINI (shown in Figure ), an adaptive GC‐aware load balancing strategy that automatically self‐configures based on the GC characteristics of a clustered application (typically located within a data centre). Internally, TRINI leverages on MaGC forecast to decide on the best way to balance the workload among the application nodes.…”

Section: Introductionmentioning

confidence: 99%

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TRINI: an adaptive load balancing strategy based on garbage collection for clustered Java systems

et al. 2016

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Summary 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. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Migc-cv Program Familiesmentioning

confidence: 97%

“…Based on the observed behaviours, three M i G C C V program families were experimentally identified : low ( M i G C C V ≤0.1), medium (0.1< M i G C C V <1.0), and high (

MiG C_{CV} \geq

1.0). For each family, a FWS trending function was derived, focusing on those MaGCs that benefit from using the increments in MiGC history (while leaving the outliers out of the trend).…”

Section: Trini: An Adaptive Garbage Collection‐aware Load Balancing Smentioning

confidence: 99%

“…Based on the observed behaviours, three M iGC C V program families were experimentally identified [13]:…”

Section: Migc-cv Program Familiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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TRINI: an adaptive load balancing strategy based on garbage collection for clustered Java systems

et al. 2016

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“…The execution time involved the lapse between starting the plagiarism detection process until getting the consolidated results' report. Garbage collection (GC) was also monitored as it is a critical performance concern in Java [17] (which is the language used to develop LOUPE's prototype). Finally, all experiments were done in an isolated test environment using a machine running Windows 8.1 64-bit, with 8 GB of RAM and a CPU at 2.20Ghz.…”

Section: A Experimental Setupmentioning

confidence: 99%

A unified approach to automate the usage of plagiarism detection tools in programming courses

Portillo-Dominguez

Ayala-Rivera

Murphy

et al. 2017

2017 12th International Conference on Computer Science and Education (ICCSE)

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PHOEBE: an automation framework for the effective usage of diagnosis tools in the performance testing of clustered systems

et al. 2017

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The identification of performance issues and the diagnosis of their root causes are time-consuming and complex tasks, especially in clustered environments. To simplify these tasks, researchers have been developing tools with built-in expertise for practitioners. However, various limitations exist in these tools that prevent their efficient usage in the performance testing of clusters (e.g. the need of manually analysing huge volumes of distributed results). In a previous work, we introduced a policy-based adaptive framework (PHOEBE) that automates the usage of diagnosis tools in the performance testing of clustered systems, in order to improve a tester's productivity, by decreasing the effort and expertise needed to effectively use such tools. This paper extends that work by broadening the set of policies available in PHOEBE, as well as by performing a comprehensive assessment of PHOEBE in terms of its benefits, costs and generality (with respect to the used diagnosis tool). The performed evaluation involved a set of experiments in assessing the different trade-offs commonly experienced by a tester when using a performance diagnosis tool, as well as the time savings that PHOEBE can bring to the performance testing and analysis processes. Our results have shown that PHOEBE can drastically reduce the effort required by a tester to do performance testing and analysis in a cluster. PHOEBE also exhibited consistent behaviour (i.e. similar time-savings and resource utilisations), when applied to a set of commonly used diagnosis tools, demonstrating its generality. Finally, PHOEBE proved to be capable of simplifying the configuration of a diagnosis tool. This was achieved by addressing the identified trade-offs without the need for manual intervention from the tester. PHOEBE is implemented with the multi-agent architecture depicted in Figure 1. There, it can be seen how PHOEBE is composed of three types of agents: the control agent is responsible for interacting with the load testing tool to know when the test starts and ends. It is also responsible for evaluating the policies and propagating the decisions to the other nodes. Meanwhile, the application node agent is responsible for performing the required tasks in each application node (e.g. sampling collection or sending the collected samples to the diagnosis tool). Finally, the diagnosis tool Here, the objective was to evaluate the potential trade-off between the number of samples concurrently processed by a diagnosis tool and the number of resources it requires to process the samples. The following sections describe this experiment and its results. PHOEBE 1863 7.1. Experiment #4: proposed policies assessmentThe objective of this experiment was to evaluate the behaviour of PHOEBE, as well as the set of proposed policies, in order to assess how well they have fulfilled their purpose of addressing the identified trade-offs without the need for manual intervention from the tester. The following sections describe this experiment and its results.7.1.1. Experimental set-up....

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Adaptive GC-Aware Load Balancing Strategy for High-Assurance Java Distributed Systems

Cited by 13 publications

References 22 publications

TRINI: an adaptive load balancing strategy based on garbage collection for clustered Java systems

TRINI: an adaptive load balancing strategy based on garbage collection for clustered Java systems

A unified approach to automate the usage of plagiarism detection tools in programming courses

PHOEBE: an automation framework for the effective usage of diagnosis tools in the performance testing of clustered systems

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