An Energy-Aware Virtual Machine Consolidation Framework for Cloud Datacenters Using Deep Reinforcement Learning

International Journal of Computer Science and Technology | Vol. 3, No. 3, March 2022 | pp. 17–24

Research Article

Title: An Energy-Aware Virtual Machine Consolidation Framework for Cloud Datacenters Using Deep Reinforcement Learning

Names of Authors: Linus Bergqvist¹, Elsa Lindstrom², and Oscar Nilsson³

Authors’ Affiliations:
¹Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden
²School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden
³Department of Information Technology, Uppsala University, Uppsala, Sweden

Abstract: Cloud computing centers generate substantial carbon footprints and incur high operational expenditures due to the continuous underutilization of physical server hardware. Dynamic virtual machine (VM) consolidation reduces energy consumption by migrating active VMs out of underutilized host nodes, allowing idle physical hardware to transition into low-power sleep modes. However, excessive VM migration activities can cause severe host resource contention and induce service level agreement (SLA) performance degradation. This paper presents an energy-aware VM consolidation framework that utilizes a deep reinforcement learning architecture based on the Asynchronous Advantage Actor-Critic (A3C) algorithm. The consolidation task is formulated as a Markov decision process where state signals capture host CPU loads, RAM utilization parameters, and network input-output traffic. The A3C neural networks learn an optimal consolidation policy that dynamically triggers VM migrations, balancing host energy savings against potential SLA violations. Simulations using standard cloud workload traces show that the proposed reinforcement learning framework cuts overall datacenter power consumption by 24.3% compared to static threshold-based migration approaches. Crucially, total VM migration counts drop by 31.5%, maintaining system performance stability under highly fluctuating traffic conditions.

Keywords: Cloud computing, Virtual machine consolidation, Deep reinforcement learning, Energy efficiency, Green computing, Resource orchestration

Manuscript Timeline: Received: December 08, 2021; Revised: January 19, 2022; Accepted: February 14, 2022; Published: March 1, 2022

Citation: Bergqvist, L., Lindstrom, E., & Nilsson, O. (2022). An energy-aware virtual machine consolidation framework for cloud datacenters using deep reinforcement learning. International Journal of Computer Science and Technology, 3(3), 17–24. DOI: 10.46882/2022/IJCST/000027

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