Scholarlink

<p>Cloud computing has emerged as the backbone of modern digital infrastructure, offering scalability, flexibility, and cost efficiency through its on-demand and pay-as-you-go models. However, the exponential growth in virtualized workloads has intensified the challenges of optimal resource allocation and energy management in large-scale data centers. Existing methods often rely on static or heuristic bin-packing strategies that fail to dynamically adapt to fluctuating workloads, leading to resource imbalance and increased power consumption. The primary objective of this work is to minimize energy consumption and enhance utilization efficiency by optimizing multidimensional resources, including CPU, RAM, and bandwidth, through dynamic learning. A Quadrant-based Difference (QBD) mechanism is proposed to handle the 3D resources. Further, QBD with Reinforcement Learning (QBDRL) for 3D Bin Packing problem is proposed for energy-efficient Virtual Machine (VM) placement. The proposed QBDRL algorithm integrates a QBD with Q-Learning to continuously adjust VM placement according to workload variations, reducing the number of active hosts while maintaining performance stability. Experimental evaluation on the CloudSim platform using PlanetLab traces demonstrates that QBD and QBDRL achieve 44.12% and 47.00% energy reduction, respectively, compared to the Random algorithm. The results confirm the proposed framework’s effectiveness for sustainable, adaptive cloud infrastructure.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

QBD and QBDRL: 3D Bin Packing Novel Approaches for Virtual Machine Placement using Reinforcement Learning for Energy Optimization in Cloud Computing Infrastructures

  • 期刊论文

  • 发表日期：

    2026年4月15日

查看全文
  • Akanksha Tandon,
  • Anand Nayyar,
  • Sanjeev Patel

摘要

Cloud computing has emerged as the backbone of modern digital infrastructure, offering scalability, flexibility, and cost efficiency through its on-demand and pay-as-you-go models. However, the exponential growth in virtualized workloads has intensified the challenges of optimal resource allocation and energy management in large-scale data centers. Existing methods often rely on static or heuristic bin-packing strategies that fail to dynamically adapt to fluctuating workloads, leading to resource imbalance and increased power consumption. The primary objective of this work is to minimize energy consumption and enhance utilization efficiency by optimizing multidimensional resources, including CPU, RAM, and bandwidth, through dynamic learning. A Quadrant-based Difference (QBD) mechanism is proposed to handle the 3D resources. Further, QBD with Reinforcement Learning (QBDRL) for 3D Bin Packing problem is proposed for energy-efficient Virtual Machine (VM) placement. The proposed QBDRL algorithm integrates a QBD with Q-Learning to continuously adjust VM placement according to workload variations, reducing the number of active hosts while maintaining performance stability. Experimental evaluation on the CloudSim platform using PlanetLab traces demonstrates that QBD and QBDRL achieve 44.12% and 47.00% energy reduction, respectively, compared to the Random algorithm. The results confirm the proposed framework’s effectiveness for sustainable, adaptive cloud infrastructure.