Load forecasting applied to soft real-time web clusters
Proceedings of the 2010 ACM Symposium on Applied Computing
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There is growing incentive to reduce the power consumed by large-scale data centers that host online services such as banking, retail commerce, and gaming. Virtualization is a promising approach to consolidating multiple online services onto a smaller number of computing resources. A virtualized server environment allows computing resources to be shared among multiple performance-isolated platforms called virtual machines. By dynamically provisioning virtual machines, consolidating the workload, and turning servers on and off as needed, data center operators can maintain desired service-level agreements with end users while achieving higher server utilization and energy efficiency. This thesis develops an online resource provisioning framework for combined power and performance management in a virtualized computing environment serving session-based workloads. We pose this management problem as one of sequential optimization under uncertainty and solve it using limited lookahead control (LLC), a form of model-predictive control. The approach accounts for the switching costs incurred while provisioning physical and virtual machines, and explicitly encodes the risk of provisioning resources in an uncertain and dynamic operating environment. We experimentally validate the control framework on a multi-tier e-commerce architecture hosting multiple online services. When managed using LLC, the cluster saves, on average, 41% in power-consumption costs over a twenty-four hour period when compared to a system operating without dynamic control. The overhead of the controller is low, compared to the control interval, on the order of a few seconds. We also use trace-based simulations to analyze LLC performance on server clusters larger than our testbed, and show how concepts from approximation theory can be used to further reduce the computational burden of controlling large systems.