An Analytical Overview Of Virtual Machine Load Balancing Scheduling Algorithms with their Comparative Case Study

TL;DR

This paper provides an analytical comparison of three virtual machine load balancing algorithms—Particle Swarm Optimization, Multi-objective Optimization, and Active Monitoring—highlighting their features, advantages, limitations, and applications in cloud computing.

Contribution

It offers a comprehensive analytical overview and comparative case study of key VM load balancing algorithms, emphasizing their suitability for cloud environments.

Findings

Particle Swarm Optimization effectively balances load with low latency.

Multi-objective Optimization addresses multiple performance metrics simultaneously.

Active Monitoring Algorithm provides real-time load adjustments.

Abstract

Efficient virtual machine load balancing scheduling is crucial in cloud computing to optimize resource utilization and system performance. To address this issue, several load balancing scheduling algorithms have been proposed, including Particle Swarm Optimization, Multi-objective Optimization, and the Active Monitoring Algorithm. This paper provides an analytical overview of these three algorithms, discussing their key features, advantages, and limitations. It contains an analysis of VM Load Balancing Scheduling Algorithms, examining their advantages, disadvantages, and applications. As the industry shifts towards adopting Cloud Technologies, optimally load balancing client requests to servers becomes essential. It is crucial for cloud providers to adapt technologies that prevent latency issues for their customers. The algorithms most commonly used in load balancers are analytically discussed.