An online service-oriented performance profiling tool for cloud computing systems
Frontiers of Computer Science: Selected Publications from Chinese Universities
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The performance of multi-service applications are known to be determined mainly by the interactions between workload and behaviors of the application. The change of workload can lead to dynamic service demands on system resources, and even cause dynamic bottleneck switches between services inside the application. In this paper, to profiling large-applications' behaviors, and help to locate the bottleneck and optimize their capacities, we focus on modeling their behavior according to the workload. Although this topic has been well studied at testing stage, building such a model under live workload remains a challenge, because the workload and application behaviors are time-varying. To tackle this problem, we propose an adaptive approach to build and rebuild performance model according to log files. Both the user behaviors and their corresponding internal service relations are modeled, and the CPU time consumed by each service is also obtained through Kalman filter, which can "absorb" some level of noise in real-world data. Our model can explain the behaviors of both the whole application and the individual services, and provide valuable information for capacity planning and bottleneck detection. At last, our work is evaluated with TPC-W bench mark, whose results can demonstrate the effectiveness of our approach.