Dynamic provisioning of server boxes to applications entails an inherent performance-power trade-off for the service provider, a trade-off that has not been studied in detail. The optimal number of replicas to be dynamically provisioned to an application is ultimately the configuration that results in the highest revenue. The service provider should thus dynamically provision resources for an application only as long as the resulting reward from hosting more clients exceeds its operational costs for power and cooling. We introduce a novel cost-aware dynamic provisioning approach for the database tier of a dynamic content site. Our approach employs Support Vector Machine regression for learning a dynamically adaptive system model. We leverage this lightweight on-line learning approach for two cost-aware dynamic provisioning techniques. The first is a temperature-aware scheme which avoids temperature hot-spots within the set of provisioned machines, and hence reduces cooling costs. The second is a more general cost-aware provisioning technique using a utility function expressing monetary costs for both performance and power.

Dynamic provisioning of server boxes to applications entails an inherent performance-power trade-off for the service provider, a trade-off that has not been studied in detail. The optimal number of replicas to be dynamically provisioned to an application is ultimately the configuration that results in the highest revenue. The service provider should thus dynamically provision resources for an application only as long as the resulting reward from hosting more clients exceeds its operational costs for power and cooling. We introduce a novel cost-aware dynamic provisioning approach for the database tier of a dynamic content site. Our approach employs Support Vector Machine regression for learning a dynamically adaptive system model. We leverage this lightweight on-line learning approach for two cost-aware dynamic provisioning techniques. The first is a temperature-aware scheme which avoids temperature hot-spots within the set of provisioned machines, and hence reduces cooling costs. The second is a more general cost-aware provisioning technique using a utility function expressing monetary costs for both performance and power.