Combined Power and Performance Management of Virtualized Computing Environments Serving Session-Based Workloads

摘要

This paper 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 when provisioning 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 server cluster supporting three online services. When managed using LLC, our cluster setup saves, on average, 41% in power-consumption costs over a twenty-four hour period when compared to a system operating without dynamic control. Finally, we 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.