Dynamic workload migration over optical backbone network to minimize data center electricity cost

As more organizations rapidly adopt cloud services, energy consumption in data centers (DCs) is increasing such that today Information and Communication Technology (ICT) has become a major consumer of energy. A large portion of ICT energy consumption is used to power servers running in DCs and the network they use to communicate. In this study, we consider that, often, energy cost at a particular DC is related to the electricity price regulated by Independent System Operators / Regional Transmission Organizations (ISOs/RTOs). As these prices vary in time and depend on the geographical locations of the DCs, recent studies have shown that the spatio-temporal variations of electricity price can be exploited to reduce electricity cost. While most prior works consider a quasi-static scenario with known workload patterns, our study proposes a dynamic workload-aware algorithm that exploits the spatio-temporal variations of electricity costs with the goal to minimize the energy cost in ICT. Our algorithm uses dynamic request rerouting and live virtual machine (VM) migration to move workloads to DCs with lower electricity cost. We consider VM migration cost (including electricity cost at optical backbone network nodes), bandwidth constraints for migration, VM consolidation, constraints from Service Level Agreement (SLA), and administrative overhead of VM migration. Our simulation studies show that the proposed algorithm reduces operational cost and improves energy efficiency of data centers significantly.