  With the increasing popularity of Cloud computing and Mobile computing,
individuals, enterprises and research centers have started outsourcing their IT
and computational needs to on-demand cloud services. Recently geographical load
balancing techniques have been suggested for data centers hosting cloud
computation in order to reduce energy cost by exploiting the electricity price
differences across regions. However, these algorithms do not draw distinction
among diverse requirements for responsiveness across various workloads. In this
paper, we use the flexibility from the Service Level Agreements (SLAs) to
differentiate among workloads under bounded latency requirements and propose a
novel approach for cost savings for geographical load balancing. We investigate
how much workload to be executed in each data center and how much workload to
be delayed and migrated to other data centers for energy saving while meeting
deadlines. We present an offline formulation for geographical load balancing
problem with dynamic deferral and give online algorithms to determine the
assignment of workload to the data centers and the migration of workload
between data centers in order to adapt with dynamic electricity price changes.
We compare our algorithms with the greedy approach and show that significant
cost savings can be achieved by migration of workload and dynamic deferral with
future electricity price prediction. We validate our algorithms on MapReduce
traces and show that geographic load balancing with dynamic deferral can
provide 20-30% cost-savings.
