Operational cost minimization of distributed data centers through the provision of fair request rate allocations while meeting different user SLAs

Data centers as computing infrastructures for cloud services have been growing in both number and scale. However, they usually consume enormous amounts of electricity that incur high operational costs of cloud service providers. Minimizing these operational costs thus becomes one main challenge in cloud computing. In this paper, we study the operational cost minimization problem in a distributed cloud computing environment that not only considers fair request rate allocations among web portals but also meets various Service Level Agreements (SLAs) between users and the cloud service provider, with an objective to maximize the number of user requests admitted while keeping the operational cost minimized, by exploiting the electricity diversity. To this end, we first propose an adaptive operational cost optimization framework that incorporates time-varying electricity prices and dynamic user request rates. We then devise a fast approximation algorithm with a provable approximation ratio for the problem, by utilizing network flow techniques. Finally, we evaluate the performance of the proposed algorithm through experimental simulations, using real-life electricity price data sets. Experimental results demonstrate that the proposed algorithm is very promising, and the solution obtained is nearly optimal.