A Stackelberg game to derive the limits of energy savings for the allocation of data center resources
Energy-related costs are becoming one of the largest contributors to the overall cost of operating a data center, whereas the degree of data center utilization continues to be very low. An energy-aware dynamic provision of resources based on the consolidation of existing application instances can simultaneously address the underutilization of servers while greatly reducing energy costs. The economics behind energy costs cannot be treated separately from resource provision and allocation. However, current scheduling techniques based on market mechanisms do not specifically deal with such a scenario. To establish the upper bound of energy savings, we model the problem of minimizing energy consumption when allocating resources to networked applications as a Stackelberg leadership game. The model is applied to a proportional-share mechanism in which resource providers can maximize profit by minimizing energy costs, while users can select resources that ensure that their minimum requirements are satisfied. We show that our mechanism can determine the optimal set of resources switched on and off, while maintaining user service level agreements (SLAs) even in realistic conditions considering incomplete information. (C) 2012 Elsevier B.V. All rights reserved.
stackelberg game energy efficiency incentive mechanism large-scale shared computational infrastructures