Competitive online algorithms for geographical load balancing in data centers with energy storage

Geographical load balancing takes advantage of the regional differences in dynamic electricity rates by shifting computing tasks among geographically distributed data centers. Since energy storage is becoming an integral part of data centers, one can maximize the benefit of the temporal and spatial fluctuations of electricity rates by combining geographical load balancing and energy storage management. Previously, the problem of integrated geographical load balancing with energy storage has been studied based on Lyapunov stochastic optimization approach, which relies on asymptotic analysis by averaging over infinite time horizon and arbitrarily large energy storage. In this paper, we present a competitive online algorithmic approach, which can be applied to finite time horizon and small-to-medium energy storage with a worst-case guarantee from the offline optimal solutions. By simulations on real-world data, it is observed that our competitive online algorithms can significantly outperform Lyapunov optimization algorithm.