Distributed Energy Management for Multiple Data Centers With Renewable Resources and Energy Storages

For Internet and cloud computing service providers, running massive geo-distributed data centers incurs prodigious electricity cost and water consumption as well as carbon emission rooted in electricity generation. Thus, it is critical significant for providers to lower down the operation cost of data centers. In this article, we investigate the problem of energy management for geo-distributed data centers with renewable resources and energy storages. We aim to minimize the long-term operation cost including electricity cost, water consumption, and carbon emission by leveraging the spatiotemporal diversity of these system states. To this end, we first formulate the cost minimization problem as a stochastic optimization problem, then we adopt the Lyapunov optimization technique to design a close-to-optimal online algorithm which only needs the current system information and achieves a delicate tradeoff between system cost and performance of delay tolerant workloads. To reduce the computational complexity and unnecessary communication, we further propose a distributed algorithm based on the distributed computing framework alternating direction method of multipliers (ADMM), which enables each data center to make their own control decisions. Based on the real-world traces and extensive simulations, we demonstrate the effectiveness of our proposed algorithms.

Automated summary

This article investigates the energy management problem of geo-distributed data centers with renewable resources and energy storages. By leveraging the spatiotemporal diversity of system states, the goal is to minimize the long-term operation cost including electricity cost, water consumption, and carbon emission. The article proposes an online algorithm using Lyapunov optimization technique and a distributed algorithm based on the ADMM framework.