Eco-Aware Online Power Management and Load Scheduling for Green Cloud Datacenters

Many of today's cloud datacenters are powered by electricity generated from brown energy (e.g., fuel fossil and oil), which directly translates into severe harm to the environment. To reduce the carbon footprint and the operating cost of datacenters, there is a clear trend to migrate to green datacenters, which are entirely (or partially) powered by renewable energy. However, given a portfolio with multiple off-and on-site power supplies (such as power grid, renewables, and batteries), it is challenging for datacenter operators to conduct efficient power management and thus meet the highly dynamic user demand. In this paper, we proposed an online algorithm, which is called EcoPower, to perform eco-aware power management and load scheduling jointly for geographically distributed cloud datacenters. Our objective is to minimize the time-average eco-aware power cost of cloud datacenters while still ensuring the quality-of-experience (QoE) constraint of user requests. To this end, we formulated the problem into a constrained stochastic optimization problem and apply the Lyapunov optimization theory to design an online control algorithm, which approaches the optimality with explicitly provable upper bounds. We also conducted extensive trace-driven simulations, and our results show that our proposed EcoPower algorithm can achieve a good balance between power cost savings, environment protection, and the user QoE, with the eco-aware power cost being cut down by over 20%. We found that wind dominant, solar complementary is a better strategy for cloud datacenters to integrate renewables into their power supply.