PHOEBE: an automation framework for the effective usage of diagnosis tools in the performance testing of clustered systems. (11th April 2017)

• Record Type: Journal Article
• Authors: Portillo‐Dominguez, A. Omar; Perry, Philip; Magoni, Damien; Murphy, John
• Is Part Of: Software, practice & experience. Volume 47:Number 11(2017)
• Page Start: 1837
• Page End: 1874
• Publication Date: 2017-04-11
• Subjects: performance testing -- performance analysis -- cluster computing -- system performance; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2500
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: 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. Copyright © 2017 John Wiley & Sons, Ltd.