Enhancing Cloud-Based Virtual Machine Migration and Consolidation with (UW-TBEA) Unpredictability-Weighted Time Backward Expectation Algorithm

Abstract

Efficient virtual machine (VM) migration and consolidation are critical for optimizing resource utilization, reducing energy consumption, and ensuring service continuity in cloud-based environments. This study introduces the Unpredictability-Weighted Time Backward Expectation Algorithm (UW-TBEA), a novel approach designed to enhance VM migration and consolidation processes. UW-TBEA dynamically adjusts migration decisions by incorporating a backward expectation framework that is weighted by the unpredictability of resource demands over time. By assessing the unpredictability of workloads, UW-TBEA prioritizes VM movements to maintain balanced resource allocation while minimizing service-level agreement (SLA) violations. Experimental results demonstrate that UW-TBEA outperforms traditional consolidation techniques by reducing migration frequency by 18%, lowering energy consumption by 22%, and decreasing SLA violations by 15%. The proposed algorithm offers a robust solution for cloud service providers to achieve cost-effective, scalable, and energy-efficient operations in dynamic and unpredictable environments.