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.

Additional Metadata
Keywords Allocation, Autonomic control, Cloud computing, Optimal deployment, Performance, Performance management, Service systems
Persistent URL dx.doi.org/10.1145/1555228.1555268
Conference 6th International Conference on Autonomic Computing, ICAC'09
Citation
Li, J. (Jim), Chinneck, J, Woodside, M. (Murray), & Litoiu, M. (Marin). (2009). Fast scalable optimization to configure service systems having cost and quality of service constraints. Presented at the 6th International Conference on Autonomic Computing, ICAC'09. doi:10.1145/1555228.1555268