An adaptive dual control framework for QoS design

Authors:
Keqiang Wu;David J. Lilja;Haowei Bai
Affiliations:
Intel Corporation, Santa Clara, USA;Electrical and Computer Engineering, University of Minnesota, Minneapolis, USA;Honeywell Defense and Space Electronics Systems, Glendale, USA
Venue:
Cluster Computing
Year:
2007

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Abstract

The widespread deployment of the advanced computer technology in business and industries has demanded the high standard on quality of service (QoS). For example, many Internet applications, i.e. online trading, e-commerce, and real-time databases, etc., execute in an unpredictable general-purpose environment but require performance guarantees. Failure to meet performance specifications may result in losing business or liability violations. As systems become distributed and complex, it has become a challenge for QoS design. The ability of on-line identification and auto-tuning of adaptive control systems has made the adaptive control theoretical design an attractive approach for QoS design. However, there is an inherent constraint in adaptive control systems, i.e. a conflict between asymptotically good control and asymptotically good on-line identification. This paper first identifies and analyzes the limitations of adaptive control for network QoS by extensive simulation studies. Secondly, as an approach to mitigate the limitations, we propose an adaptive dual control framework. By incorporating the existing uncertainty of on-line prediction into the control strategy and accelerating the parameter estimation process, the adaptive dual control framework optimizes the tradeoff between the control goal and the uncertainty, and demonstrates robust and cautious behavior. The experimental study shows that the adaptive dual control framework mitigate the limitations of the conventional adaptive control framework. Compared with the conventional adaptive control framework under the medium uncertainty, the adaptive dual control framework reduces the deviation from the desired hit-rate ratio from 40% to 13%.