Adaptive Resource Allocation in Cloud Data Centers using Actor-Critical Deep Reinforcement Learning for Optimized Load Balancing

Abstract

This paper proposes a deep reinforcement learning-based actor-critic method for efficient resource allocation in cloud computing. The proposed method uses an actor network to generate the allocation strategy and a critic network to evaluate the quality of the allocation. The actor and critic networks are trained using a deep reinforcement learning algorithm to optimize the allocation strategy. The proposed method is evaluated using a simulation-based experimental study, and the results show that it outperforms several existing allocation methods in terms of resource utilization, energy efficiency and overall cost. Some algorithms for managing workloads or virtual machines have been developed in previous works in an effort to reduce energy consumption; however, these solutions often fail to take into account the high dynamic nature of server states and are not implemented at a sufficiently enough scale. In order to guarantee the QoS of workloads while simultaneously lowering the computational energy consumption of physical servers, this study proposes the Actor Critic based Compute-Intensive Workload Allocation Scheme (AC-CIWAS). AC-CIWAS captures the dynamic feature of server states in a continuous manner, and considers the influence of different workloads on energy consumption, to accomplish logical task allocation. In order to determine how best to allocate workloads in terms of energy efficiency, AC-CIWAS uses a Deep Reinforcement Learning (DRL)-based Actor Critic (AC) algorithm to calculate the projected cumulative return over time. Through simulation, we see that the proposed AC-CIWAS can reduce the workload of the job scheduled with QoS assurance by around 20% decrease compared to existing baseline allocation methods. The report also covers the ways in which the proposed technology could be used in cloud computing and offers suggestions for future study.