Cloud Resource Scheduling With Deep Reinforcement Learning and Imitation Learning
Wenxia Guo, Wenhong Tian, Yufei Ye, Lingxiao Xu, Kui Wu
Abstract
The cloud resource management belongs to the category of combinatorial optimization problems, most of which have been proven to be NP-hard. In recent years, reinforcement learning (RL), as a special paradigm of machine learning, has been used to tackle these NP-hard problems. In this article, we present a deep RL-based solution, called DeepRM_Plus, to efficiently solve different cloud resource management problems. We use a convolutional neural network to capture the resource management model and utilize imitation learning in the reinforcement process to reduce the training time of the optimal policy. Compared with the state-of-the-art algorithm DeepRM, DeepRM_Plus is 37.5% faster in terms of the convergence rate. Moreover, DeepRM_Plus reduces the average weighted turnaround time and the average cycling time by 51.85% and 11.51%, respectively.