Knee Point-Driven Bottleneck Detection Algorithm for Cloud Service System

Liu, Xiao Long; Zhang, Xue Bai; Chao, Hsiang; Yuan, Shyan-Ming

doi:10.1007/978-3-319-45835-9_9

Cited by 1 publication

(1 citation statement)

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“…However, all the bottleneck detection mechanisms necessitate defining the threshold of service level objective (SLO), which would be inconvenient for service providers because SLO satisfaction involves variance in specific measurable characteristics, such as availability, throughput, response time, and frequency. To simplify the detection mechanism, our previous study proposed an improved bottleneck detection algorithm based on knee point, which provides a model applicable for various services and enables the convenient identification of all potential resource bottlenecks.…”

Section: Introductionmentioning

confidence: 99%

Automatic cloud service testing and bottleneck detection system with scaling recommendation

Liu

Sheu

et al. 2019

Concurrency and Computation

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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.

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Section: Introductionmentioning

confidence: 99%