EFFICIENT RESOURCE ALLOCATION AND LOAD BALANCING ALGORITHMS IN CLOUD VIRTUALIZATION ENVIRONMENTS

Abstract

The growing demand for cloud computing services has necessitated the development of efficient resource allocation and load balancing algorithms to ensure optimal performance, scalability, and energy efficiency. This study presents a hybrid approach combining traditional and machine learning-based techniques for resource allocation and load balancing in cloud virtualization environments. We propose an algorithm designed to enhance resource utilization, reduce task completion times, improve fairness in multi-tenant environments, and optimize energy consumption. The effectiveness of the proposed algorithm is evaluated through simulations under varying workloads and cloud configurations. According to the research findings the recommended method produces superior results than both round-robin and least-connections in terms of key performance measures. The CPU utilization rate by our method reaches 93.5% while round-robin performs at 72.8% and least-connections uses 80.4%. The work completion time gets reduced by up to 30% through implementation of this method which shows increased efficiency against traditional methods. The method proposes reduced energy consumption which directly benefits the environment-friendly operations of cloud platforms. The new algorithm achieves resource distribution fairness with an index of 0.92 that surpasses the limited results recorded by traditional methods. The technique presents excellent scalability features alongside stable performance levels when managing a growing number of virtual machines which shows it works well for large cloud environments. This research promotes machine learning methods for cloud resource management which enhance operational effectiveness and reduce power usage and create fair resource distribution schemes. The analysis from our work delivers beneficial information for cloud service providers to enhance operational effectiveness through increased service capacity while minimizing environmental impact.