  Cloud service providers are distributing data centers geographically to
minimize energy costs through intelligent workload distribution. With
increasing data volumes in emerging cloud workloads, it is critical to factor
in the network costs for transferring workloads across data centers. For
geo-distributed data centers, many researchers have been exploring strategies
for energy cost minimization and intelligent inter-data-center workload
distribution separately. However, prior work does not comprehensively and
simultaneously consider data center energy costs, data transfer costs, and data
center queueing delay. 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. 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. Our
simulations show that the proposed game-theoretic technique can minimize the
cloud operating cost more effectively than existing approaches.
