Efficient Peak Shaving in a Data Center by Joint Optimization of Task Assignment and Energy Storage Management

The usage of energy storage devices in data centers has been widely studied for the purpose of peak shaving in the context of dynamic utility pricing. To effectively achieve peak shaving in a data center with energy storage capability, a joint optimization framework is proposed to solve the task assignment problem and the energy storage management problem. The power hierarchy of a data center is modeled as a tree with battery arrays connected to each node, which is a generalization of common energy storage deployment topologies including the centralized structure and the rack/server-level distributed structure. A multi-dimensional resource request model is adopted based on released cluster traces in order to capture the complex dynamics of the workload of a data center. In addition, the rate capacity effect and aging effect are considered when modeling the batteries. A progressive refinement approach is used to solve the optimization problem. Experimental results show that the proposed algorithm consistently outperforms some baseline algorithms.