LAUSR

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.