Incentive Temperature Control for Green Colocation Data Centers via Reinforcement Learning

Rongrong Wang, Duc Van Le, Jikun Kang, Rui Tan, Xue Liu

Abstract: Increasing supply air temperatures is a rule-of-thumb approach to reduce cooling energy usage of data centers (DCs). However, colocation DCs are short of incentive programs to move tenants from the current over-cooling strategy despite the expanding allowable temperature ranges of the computing equipment. This paper considers an essential incentive mechanism, in which the DC operator offers monetary incentives to offset tenants’ electricity payments. We propose an encoder-embedded multi-agent reinforcement learning solution to let the operator agent and tenant agents collaboratively find their policies for deciding the incentives and supply air temperatures, respectively, which are coupled in determining the DC’s total cooling power usage. The solution does not require the cooling power model, which is complex and in general unavailable in practice. Moreover, as each tenant agent learns in the other tenants’ latent state spaces defined by their pre-trained variational autoencoders, only encoded tenants’ states are exchanged, thereby mitigating information leakage concerns. Extensive trace-driven evaluation and comparison with three baselines show that our solution effectively incentivizes tenants to move from the over-cooling strategy and achieves substantial cooling power savings.