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. Energy-aware dynamic provision of resources based on the consolidation of existing application instances can simultaneously address under-utilization of servers while highly reducing energy costs. Thus, 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 scenario. In this paper we model the problem of minimizing energy consumption of the allocation of resources to networked applications as a Stackelberg leadership game to find an upper bound of energy saving. The model is applied to a proportional-share mechanism where resource providers can maximize their profit by minimizing energy costs while users can select resources ensuring the minimum requirements are satisfied. We show that our mechanism can determine the optimal set of resources on and off, even in realistic conditions considering incomplete information, and heterogeneous applications.

Peer Reviewed