Enhancing Energy Efficiency in Cloud DataCenters through Deformable Graph ConvolutionalNetwork-Aware Virtual Machine Placement withHybrid Swarm Bipolar Walk-Spread Optimization
TL;DR
Results show that the proposed method dramatically 27 KW lowers power consumption, improves 98% resource usage and 180 kg CO₂ decreases carbon emissions, compared to conventional VM placement techniques.
Abstract
In big cloud data centers, the best physical machine (PM) is selected using the Virtual Machine Placement(VMP) process. To address this issue, a number of approaches have been proposed. Nevertheless, the existing solutionsonly take into account a small number of resource categories, which leads to an uneven load and ultimately, theactivation of superfluous physical computers within the data center. The aim of this research is to maximize resourceusage while lowering power use and carbon footprints by integrating a Hybrid Swarm Bipolar with Walk-SpreadAlgorithm with a unique Deformable Graph Convolutional Network (DGCN-SB-WSA)-aware virtual machine placement architecture. To ensGoogleent VM scheduling and management, real-world cloud workloads are analyzedusing the Google Cluster Dataset (GCD). The Deformable Graph Convolutional Network (DGCN) dynamically modelscloud infrastructure as a graph that captures intricate relationships among PMs and VMs, enabling adaptive placementchoices. The Hybrid Swarm Bipolar with Walk-Spread Algorithm (SB-WSA) then uses a dual-phase search approach tobalance local exploitation with global exploration, minimizing premature convergence and increasing performancewhile optimizing Virtual machine (VM) allocation. Comparing the proposed method to conventional VM placementtechniques, experimental results show that it dramatically 27 KW lowers power consumption, improves 98% resourceusage and 180 kg CO₂ decreases carbon emissions.