eMRA: A Multi-Objective Resource Allocation Technique using Genetic Algorithms for Cloud Computing

Efficient resource allocation in cloud computing is crucial for balancing performance, cost, and environmental concerns. However, existing methods often struggle to address these conflicting objectives simultaneously. This paper proposes eMRA (Enhanced Method for Resource Allocation), a novel resource allocation technique that utilizes a genetic algorithm for multi-objective optimization. eMRA considers diverse objectives like cost minimization, response time reduction, and energy consumption reduction. It incorporates user preferences and dynamically adapts to changing demands, aiming to achieve an optimal balance between these objectives. The research delves into the design, implementation, and evaluation of eMRA, showcasing its potential to optimize resource allocation in cloud environments, delivering improved performance, reduced costs, and minimized energy consumption. Furthermore, the research highlights the benefits of eMRA compared to traditional single-objective approaches, paving the way for advancements in cloud resource management and overall service efficiency of 0.5 mJ energy consumption compared to the existing methods.