Autonomic Provisioning with Self-Adaptive Neural Fuzzy Control for Percentile-Based Delay Guarantee

Abstract

Autonomic server provisioning for performance assurance is a critical issue in Internet services. It is challenging to guarantee that requests flowing through a multi-tier system will experience an acceptable distribution of delays. The difficulty is mainly due to highly dynamic workloads, the complexity of underlying computer systems, and the lack of accurate performance models. We propose a novel autonomic server provisioning approach based on a model-independent self-adaptive Neural Fuzzy Control (NFC). Existing model-independent fuzzy controllers are designed manually on a trial-and-error basis, and are often ineffective in the face of highly dynamic workloads. NFC is 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. We further enhance NFC to compensate for the effect of server switching delays. Extensive simulations demonstrate that, compared to a rule-based fuzzy controller and a Proportional-Integral controller, the NFC-based approach delivers superior performance assurance in the face of highly dynamic workloads. It is robust to variation in workload intensity, characteristics, delay target, and server switching delays. We demonstrate the feasibility and performance of the NFC-based approach with a testbed implementation in virtualized blade servers hosting a multi-tier online auction benchmark.