Enhancing energy efficiency in cloud scaling: A DRL-based approach incorporating cooling power

• Published in: SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
• Main Authors: Jawaddi, Siti Nuraishah Agos; Ismail, Azlan; Shafian, Shafidah
• Format: Article
• Language: English
• Published: ELSEVIER 2023
• Subjects: Science & Technology - Other Topics; Energy & Fuels
• Online Access: https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001124397600001

The rapid growth of cloud computing significantly boosts energy usage, driven mainly by CPU operations and cooling. While cloud scaling efficiently allocates resources for changing workloads, current energy-driven methods often prioritize energy metrics combined with throughput, execution time, or SLA compliance, neglect-ing cooling power's influence on energy consumption. To bridge this gap, we propose a deep reinforcement learning (DRL)-based autoscaler that considers cooling power as a critical factor for decision-making. Our approach employs DRL to dynamically adjust cloud resources, aiming to maximize energy efficiency and meet performance objectives. DRL, unlike RL, uses neural networks to handle the extensive state-action space in cloud scaling, overcoming the challenge of limited memory capacity for storing Q-values. In this study, we evaluate the performance of our proposed solution through a simulation-based experiment. We compare the performance of the proposed DRL-based autoscalers against an RL-based autoscaler. Our findings indicate that the DDQN-based autoscaler consistently outperforms other algorithms by maintaining optimal Power Usage Effectiveness (PUE) levels and improving task execution speed during high workloads. In contrast, the DQN-based autoscaler excels at sustaining optimal PUE levels during lower task loads, with a faster convergence rate at a scaling factor of 2 compared to scaling factor 1.