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Predictive performance models are important tools that support system sizing, capacity planning, and systems management exercises. The paper introduces the Weighted Average Method (WAM) to improve the accuracy of analytic predictive performance models for systems with bursts of concurrent customers. WAM considers the customer population distribution at a system to reflect the impact of bursts. The WAM approach is robust with respect to distribution functions, including heavy-tail-like distributions, for workload parameters. The paper demonstrates the effectiveness of WAM using a case study involving a multi-tier TPC-W benchmark system.
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