Knee Point-Driven Bottleneck Detection Algorithm for Cloud Service System

Abstract

Currently, providers of Software as a service (SaaS) can use Infrastructure as a Service (IaaS) to obtain the resources required for serving customers. Performance problems in a SaaS system are difficult to diagnose, because they may be caused by various system components. This study proposes a knee point-driven bottleneck detection algorithm, the specific resource bottleneck in the target system can be detected by analyzing the collected metrics. The detection result provides a scale up recommendation for the service provider to facilitate reconfiguring the service system. The experimental results revealed that the proposed system can detect a potential bottleneck in a service system accurately. After solving the detected bottleneck the performance of the target cloud service can be improved efficiently.