PERFUME: power and performance guarantee with fuzzy MIMO control in virtualized servers
Proceedings of the Nineteenth International Workshop on Quality of Service
Autonomic Resource Management with Support Vector Machines
GRID '11 Proceedings of the 2011 IEEE/ACM 12th International Conference on Grid Computing
Regression-based resource provisioning for session slowdown guarantee in multi-tier Internet servers
Journal of Parallel and Distributed Computing
AROMA: automated resource allocation and configuration of mapreduce environment in the cloud
Proceedings of the 9th international conference on Autonomic computing
UCC '12 Proceedings of the 2012 IEEE/ACM Fifth International Conference on Utility and Cloud Computing
Future Generation Computer Systems
SCAling: SLA-driven cloud auto-scaling
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Autonomic Provisioning with Self-Adaptive Neural Fuzzy Control for Percentile-Based Delay Guarantee
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
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Autonomic server provisioning for performance assurance is a critical issue in data centers. It is important but challenging to guarantee an important performance metric, percentile-based end-to-end delay of requests flowing through a virtualized multi-tier server cluster. It is mainly due to dynamically varying workload and the lack of an accurate system performance model. In this paper, we propose a novel autonomic server allocation approach based on a model-independent and self-adaptive neural fuzzy control. There are model-independent fuzzy controllers that utilize heuristic knowledge in the form of rule base for performance assurance. Those controllers are designed manually on trial and error basis, often not effective in the face of highly dynamic workloads. We design the neural fuzzy controller as a hybrid of control theoretical and machine learning techniques. It is capable of self-constructing its structure and adapting its parameters through fast online learning. Unlike other supervised machine learning techniques, it does not require off-line training. We further enhance the neural fuzzy controller to compensate for the effect of server switching delays. Extensive simulations demonstrate the effectiveness of our new approach in achieving the percentile-based end-to-end delay guarantees. Compared to a rule-based fuzzy controller enabled server allocation approach, the new approach delivers superior performance in the face of highly dynamic workloads. It is robust to workload variation, change in delay target and server switching delays.