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Workload-aware load balancing for clustered Web servers

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5 Author(s)
Qi Zhang ; Dept. of Comput. Sci., Coll. of William & Mary, Williamsburg, VA, USA ; Riska, A. ; Sun, W. ; Smirni, E.
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We focus on load balancing policies for homogeneous clustered Web servers that tune their parameters on-the-fly to adapt to changes in the arrival rates and service times of incoming requests. The proposed scheduling policy, ADAPTLOAD, monitors the incoming workload and self-adjusts its balancing parameters according to changes in the operational environment such as rapid fluctuations in the arrival rates or document popularity. Using actual traces from the 1998 World Cup Web site, we conduct a detailed characterization of the workload demands and demonstrate how online workload monitoring can play a significant part in meeting the performance challenges of robust policy design. We show that the proposed load, balancing policy based on statistical information derived from recent workload history provides similar performance benefits as locality-aware allocation schemes, without requiring locality data. Extensive experimentation indicates that ADAPTLOAD results in an effective scheme, even when servers must support both static and dynamic Web pages.

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Parallel and Distributed Systems, IEEE Transactions on  (Volume:16 ,  Issue: 3 )