Abstract

AbstractResource allocation and power management have become challenging tasks in cloud computing. Since workloads change over time, dynamic cloud resource allocation and power management solutions are needed, that can intrinsically adapt to the demand of the resources. This paper demonstrates the use of deep reinforcement learning algorithms for the task dispatching and power management in the simulated Alibaba cluster environment with the goal of achieving better power and latency management than traditional task scheduling algorithms. In this paper, we propose a hierarchical RL model for resource allocation and power management which obtains better results in terms of latency as well as power usage, than traditional task scheduling.KeywordsDynamic resource managementReinforcement learningCloud computingTask scheduling

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