Virtual Machine Migration During Task Failure to Enhance Quality of Service

This research explores innovative approaches to enhance energy efficiency in cloud computing by automating the deployment of simulated hardware, tailoring resource allocation to individual user needs. Emphasizing the dynamic nature of load demands, the study leverages the adaptability of technology for efficient Virtual Machine (VM) migrations, particularly within edge cloud environments. Unlike traditional cloud data centers, edge clouds present challenges due to limited resources. The investigation centers on optimizing bandwidth allocation during simultaneous live VM migrations, a critical yet overlooked aspect in prior research. The main goal of this research is to provide CPU utilization, Memory utilization, Migration of tasks. While existing studies focus on accelerating single VM transfers, this research uniquely addresses multiple Virtual Machine Monitor (VMM) activities, aiming to enhance overall Quality of Service (QoS) without compromising time constraints. The work's primary focus is load balancing to sustain high QoS, gauged by SLAV metrics providing granular insights into service quality.