Energy-Efficient Joint Task Assignment and Migration in Data Centers: A Deep Reinforcement Learning Approach

Abstract

Energy-efficient task scheduling in data centers is a critical issue and has drawn wide attention. However, the task execution times are mixed and hard to estimate in a real-world data center. It has been conspicuously neglected by existing work that scheduling decisions made at tasks’ arrival times are likely to cause energy waste or idle resources over time. To fill in such gaps, in this paper, we jointly consider assignment and migration for mixed duration tasks and devise a novel energy-efficient task scheduling algorithm. Task assignment can improve resource utilization, and migration is required when long-running tasks run in low-load servers. Specifically: 1) We formulate mixed duration task scheduling as a large-scale Markov Decision Process (MDP) problem; 2) To solve such a large-scale MDP problem, we design an efficient Deep Reinforcement Learning (DRL) algorithm to make assignment and migration decisions. To make the DRL algorithm more practical in real scenarios, multiple optimizations are proposed to achieve online training; 3) Experiments with real-world data have shown that our algorithm outperforms the existing baselines 14% on average in terms of energy consumption while keeping the same level of Quality of Service (QoS).