Automatic cloud service testing and bottleneck detection system with scaling recommendation

Xiaolong Liu, Ruey Kai Sheu*, Win Tsung Lo, Shyan-Ming Yuan*

*Corresponding author for this work

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Performance problems in a cloud service are difficult to diagnose because they may be caused by various system components. This study proposes an automatic cloud service testing and bottleneck detection system that is applicable to different types of services. With the proposed test module, the user can customize test scenarios to automatically test and collect the corresponding metrics of the target service. Afterward, the proposed bottleneck detection algorithm analyzes the collected metrics and determines whether a bottleneck is presented in the target system. The bottleneck detection module also provides a scaling recommendation for the service provider to facilitate the service system reconfiguration. The experimental results reveal that the proposed system could detect a potential bottleneck in a service system accurately. In accordance with the scaling recommendation, the performance of the target cloud service can be improved efficiently after reconfiguration. Therefore, usage of the proposed system can ensure a high quality of service, and the service level objective could be fulfilled.

Original languageEnglish
Article numbere5161
JournalConcurrency Computation
DOIs
StatePublished - 10 Jan 2020

Keywords

  • automatic testing
  • bottleneck detection
  • cloud computing
  • quality of service (QoS)
  • scaling

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