Paper Info
Title
PHOEBE: an automation framework for the effective usage of diagnosis tools in the performance testing of clustered systems.
Abstract
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 (C) 2017 John Wiley & Sons, Ltd.
Year
DOI
Venue
2017
10.1002/spe.2500
SOFTWARE-PRACTICE & EXPERIENCE
Keywords
Field
DocType
performance testing,performance analysis,cluster computing,system performance
Architecture,Software engineering,Computer science,Petroleum engineering,Response time,Automation,Throughput,Operating system,Generality,Computer cluster
Journal
Volume
Issue
ISSN
47.0
11.0
0038-0644
Citations 
PageRank 
References 
2
0.43
23
Authors
4
Name
Order
Citations
PageRank
A. Omar Portillo-Dominguez1255.68
Philip Perry221.10
Damien Magoni324030.36
John Murphy4526.72