文献类型：期刊文献

英文题名：Affinity and Interference-Aware Service Deployment for Energy Efficiency in Cloud Data Centers: A Deep Reinforcement Learning Approach

作者：Xu, Jin[1]; Yu, Huiqun[1,2]; Fan, Guisheng[1,2]; Liu, Shengwei[3]; Zhang, Hengrun[1]; Chen, Liqiong[4]

机构：[1] East China University of Science and Technology, Department of Computer Science and Engineering, Shanghai, 200237, China; [2] Shanghai Engineering Research Center of Smart Energy, Shanghai, 201103, China; [3] Shanghai Data Exchange Corporation, Shanghai, 201203, China; [4] Shanghai Institute of Technology, Department of Computer Science and Engineering, Shanghai, 201418, China

年份：2025

起止页码：759

外文期刊名：Proceedings - 2025 IEEE 49th Annual Computers, Software, and Applications Conference, COMPSAC 2025

收录：EI(收录号：20253819196514)

语种：英文

外文关键词：Behavioral research - Cloud computing - Data centers - Data handling - Deep learning - Deep reinforcement learning - Energy utilization - Green computing - Information management - Information services - Learning algorithms - Markov processes - Optimization - Quality of service - Telecommunication services

摘要：Cloud computing has revolutionized data center management by providing scalable and efficient resources for processing and data management. However, deploying containerd-based services in data centers presents significant challenges: (1) Active servers that are underutilized result in high energy consumption, necessitating optimization for energy efficiency; (2) Affinity requirements between services and servers must be considered to ensure appropriate deployments; (3) Quality of Service (QoS) requirements must be met, particularly to avoid performance interference when multiple services are deployed on the same server. To address these challenges, we propose a novel algorithm, Affinity-Interference Energy Deployment (AIED), based on Deep Reinforcement Learning (DRL). This algorithm strategically consolidates services onto fewer servers to optimize energy efficiency while adhering to stringent QoS and affinity constraints. By employing a demand-supply model to quantify QoS requirements and formulating the deployment challenge as a Markov Decision Process (MDP), our algorithm dynamically adapts to fluctuating demands and resource availability. Extensive simulations demonstrate that AIED significantly outperforms existing baseline strategies, reducing energy consumption while ensuring robust compliance with both QoS and affinity constraints. ? 2025 IEEE.