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

  • Authors:
  • Jim (Zhanwen) Li;John Chinneck;Murray Woodside;Marin Litoiu

  • Affiliations:
  • Carleton University, Ottawa, ON, Canada;Carleton University, Ottawa, ON, Canada;Carleton University, Ottawa, ON, Canada;York University, Toronto, ON, Canada

  • Venue:
  • ICAC '09 Proceedings of the 6th international conference on Autonomic computing
  • Year:
  • 2009

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Abstract

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) sub-problem, 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.