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Automated and Agile Server ParameterTuning by Coordinated Learning and Control
April 2014 (vol. 25 no. 4)
pp. 876-886
Xiaobo Zhou, Dept. of Comput. Sci., Univ. of Colorado, Colorado Springs, CO, USA
Changjun Jiang, Dept. of Comput. Sci. & Technol., Tongji Univ., Shanghai, China
Palden Lama, Dept. of Comput. Sci., Univ. of Colorado, Colorado Springs, CO, USA
Yanfei Guo, Dept. of Comput. Sci., Univ. of Colorado, Colorado Springs, CO, USA
Automated server parameter tuning is crucial to performance and availability of Internet applications hosted in cloud environments. It is challenging due to high dynamics and burstiness of workloads, multi-tier service architecture, and virtualized server infrastructure. In this paper, we investigate automated and agile server parameter tuning for maximizing effective throughput of multi-tier Internet applications. A recent study proposed a reinforcement learning based server parameter tuning approach for minimizing average response time of multi-tier applications. Reinforcement learning is a decision making process determining the parameter tuning direction based on trial-and-error, instead of quantitative values for agile parameter tuning. It relies on a predefined adjustment value for each tuning action. However it is nontrivial or even infeasible to find an optimal value under highly dynamic and bursty workloads. We design a neural fuzzy control based approach that combines the strengths of fast online learning and self-adaptiveness of neural networks and fuzzy control. Due to the model independence, it is robust to highly dynamic and bursty workloads. It is agile in server parameter tuning due to its quantitative control outputs. We implemented the new approach on a testbed of virtualized data center hosting RUBiS and WikiBench benchmark applications. Experimental results demonstrate that the new approach significantly outperforms the reinforcement learning based approach for both improving effective system throughput and minimizing average response time.
Index Terms:
Servers,Tuning,Fuzzy control,Time factors,Throughput,Neurons,Internet,neural fuzzy control,Automated server parameter tuning,internet applications,autonomic computing
Citation:
Xiaobo Zhou, Changjun Jiang, Palden Lama, Yanfei Guo, "Automated and Agile Server ParameterTuning by Coordinated Learning and Control," IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 4, pp. 876-886, April 2014, doi:10.1109/TPDS.2013.115
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