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.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/11143 |
| Date | 30 July 2008 |
| Creators | Ghanbari, Saeed |
| Contributors | Amza, Cristiana |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
| Format | 1095924 bytes, application/pdf |
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