GcLite: An Expert Tool for Analyzing Garbage Collection Behavior

Angelopoulos, Vasileios; Parsons, Trevor; Murphy, John; O’Sullivan, Pat

doi:10.1109/compsacw.2012.93

Cited by 5 publications

(10 citation statements)

References 3 publications

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“…A special challenge, documented by multiple authors [3,18], is that current performance tools heavily rely on human experts to understand their output. Also multiple sources are commonly required to diagnose performance problems, especially in highly distributed environments, such as the Cloud.…”

Section: Introductionmentioning

confidence: 99%

“…This increases the expertise required to do performance analysis, which is usually held by only a small number of experts inside an organization [23]. Therefore it could potentially lead to bottlenecks where certain activities can only be done by these experts, impacting the productivity of the testing teams [3]. This risk is higher in Cloud environments, as vendors commonly promise up to 24x7 availability of their services.…”

Section: Introductionmentioning

confidence: 99%

“…To simplify the performance analysis and diagnosis, hence increasing the productivity and reducing the dependency on human experts, many researchers have been developing tools with built-in expertise [2,3]. However, various limitations exist in these tools that prevent their efficient usage in the performance testing of Cloud environments.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automated WAIT for Cloud-Based Application Testing

Portillo-Dominguez

Miao

Murphy

et al. 2014

2014 IEEE Seventh International Conference on Software Testing, Verification and Validation Workshops

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated WAIT for Cloud-Based Application Testing

Portillo-Dominguez

Miao

Murphy

et al. 2014

2014 IEEE Seventh International Conference on Software Testing, Verification and Validation Workshops

Self Cite

View full text Add to dashboard Cite

“…Under these conditions, it is not surprising that performance testing is complex and time-consuming. A special challenge is that performance tools heavily rely on human experts to be configured properly and to interpret their outputs [2,14]. As this expertise is usually held by only a small number of people inside an organization, this issue could lead to bottlenecks, impacting the productivity of testing teams [2].…”

Section: Introductionmentioning

confidence: 99%

“…To simplify the performance analysis and testing, many researchers have been developing tools with built-in expertise [1,2]. However, limitations exist in these tools that prevent their efficient usage in the performance testing of highly distributed environments.…”

Section: Introductionmentioning

confidence: 99%

Towards an automated approach to use expert systems in the performance testing of distributed systems

Portillo-Dominguez

Wang

Murphy

et al. 2014

Proceedings of the 2014 Workshop on Joining AcadeMiA and Industry Contributions to Test Automation and Model-Based Testing

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Performance testing in distributed environments is challenging. Specifically, the identification of performance issues and their root causes are time-consuming and complex tasks which heavily rely on expertise. To simplify these tasks, many researchers have been developing tools with built-in expertise. However limitations exist in these tools, such as managing huge volumes of distributed data, that prevent their efficient usage for performance testing of highly distributed environments. To address these limitations, this paper presents an adaptive framework to automate the usage of expert systems in performance testing. Our validation assessed the accuracy of the framework and the time savings that it brings to testers. The results proved the benefits of the framework by achieving a significant decrease in the time invested in performance analysis and testing.

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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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GcLite: An Expert Tool for Analyzing Garbage Collection Behavior

Cited by 5 publications

References 3 publications

Automated WAIT for Cloud-Based Application Testing

Automated WAIT for Cloud-Based Application Testing

Towards an automated approach to use expert systems in the performance testing of distributed systems

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

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