Modeling of the Data Center Resource Management Using Reinforcement Learning

Cloud data centers are most dynamic systems in a modern digital world. To deliver the high-performance and fault-tolerant IT services to end users effectively it is necessary to develop new methods for data center resource management while adapting to the emergence of new requirements. In this paper, the authors refine and evaluate the previously proposed method for cloud data center resource management based on the reinforcement learning approach. The proposed method takes into account the power consumption and the number of SLA violations in the management policy. The power consumption is managed by switching physical servers to active or sleep state depending on current utilization of three resources: CPU, memory, and network bandwidth. The proposed reinforcement learning agent allows to determine the optimal policy for managing the physical servers without creating an environment model and preliminary information about the workload. The evaluation results show that the proposed method allows to decrease the SLA violation time, to serve more VM schedule requests when the number of VMs is changing frequently, and to decrease the utilization of data center network due to decreased number of migrations.