Energy Efficient Decentralized Geographical Load Balancing via Dynamic Deferral of Workload

Zeenat Islam, Md. Moniruzzaman, Muhammad Abdullah Adnan

Abstract: Integration of on-demand Cloud services has become an orthodox approach adopted by enterprises of all scales, especially over the past decade. Thus, efficient load balancing techniques in data centers that perform intensive cloud computations become increasingly necessary to reduce energy costs. The essence of cloud computing lies within data centers being located in diverse locations, thus in different time zones. Thus, we take advantage of spatial and temporal variations of the data centers in our load balancing decisions. We demonstrate our design of a decentralized online algorithm for geographical load balancing via dynamic deferral of arriving workload from the data center. It responds with reduced energy costs by adapting to the dynamic price changes in its region via a head start of predicting prices in the real market. We compare our algorithm with the centralized approach without future price predictions and have achieved cost savings. We validate our model by experiments run on Google traces, and show that geographical load balancing with dynamic deferral can provide up to 32% reduction in costs to execute arriving load in data centers with both temporal and geographical variations.