Energy and Network Aware Workload Management for Geographically Distributed Data Centers

Cloud service providers are distributing data centers geographically to minimize energy costs through intelligent workload distribution. With increasing volumes in emerging cloud workloads, it is critical factor the network for transferring workloads across centers. For geo-distributed centers, many researchers have been exploring strategies cost minimization and inter-data-center distribution separately. However, prior work does not comprehensively simultaneously consider center costs, transfer queueing delay. In this paper, we propose a novel game theory-based management framework that takes holistic approach operating problem by making scheduling decisions aware of Our performs considers heterogeneity compute capability, cooling power, interference effects from task co-location servers, time-of-use electricity pricing, renewable energy, net metering, peak demand pricing distribution, pricing. simulations show proposed game-theoretic technique can more effectively than existing approaches.