Automated and agile server parameter tuning with learning and control

作者：Guo Yanfei^*; Lama Palden; Zhou Xiaobo

来源：2012 IEEE 26th International Parallel and Distributed Processing Symposium, IPDPS 2012, 2012-05-21 To 2012-05-25.

DOI：10.1109/IPDPS.2012.66

• Benchmark applications
• Blade servers
• Burstiness
• Data centers
• Decision making process
• Effective throughput
• High dynamic
• Internet application
• Learning-based approach
• Multi-tier applications
• Multi-tier internets
• Multi-tier service
• Online learning
• Optimal values
• Parameter-tuning
• Quantitative values
• Self-adaptive
• Server infrastructure
• System throughput

Server parameter tuning in virtualized data centers is crucial to performance and availability of hosted Internet applications. 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-adaptive ness 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 implement the new approach on a test bed of virtualized HP Pro Liant blade servers hosting RUBiS 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.

• 出版日期2012