Deep Reinforcement Learning for Intelligent Cloud Resource Management

For cloud computing, elaborately managing resources and workloads to optimize various metrics such as performance, cost and energy is of strategic importance, but by no means trivial. Traditional model- or heuristic-based solutions are highly knowledge- and labor-intensive, as well as problem-specific. Recently, with the booming of AI, researchers in cloud computing community are motivated to revisit cloud resource/workload management problem by applying the emerging deep reinforcement learning (DRL) method. In this paper, we first identify the motivations of applying DRL to the long-standing and challenging cloud management problems. Then we provide a selective survey of the recent advances with analysis of their design principles and benefits. Based on those pilot attempts, we summarize the general workflow and conduct a case study to illustrate how to apply DRL for intelligent cloud resource/workload management. The goal of this article is to provide a broad guideline on DRL-based intelligent cloud management to help stimulate researchers to develop innovative algorithms, frameworks and standards.