Fast scalable optimization to configure service systems having cost and quality of service constraints

Large complex service centers must provide many services to many users with separate service contracts, while managing their overall costs. A scalable hybrid optimization procedure is described for a minimum-cost deployment of services on nodes, taking into account processing requirements and resource contention. This is a heuristic for a problem which is in general NP-hard. It iterates between a fast linear programming (LP) subproblem, and a nonlinear performance model, both of which scale easily to thousands of services. The approach can be adapted to minimize cost subject to performance constraints, or to optimize a combined quality of service measure subject to cost constraints. It can be combined with tracked performance models to periodically re-optimize deployment for autonomic QOS management.

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