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

Cloud service providers are distributing data centers geographically to minimize energy costs through intelligent workload distribution.

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