Multi-Objective Genetic Algorithms for Dynamic Resource Optimization in Cloud Computing

Cloud computing is now a promising model for arbitrary access and pay-per-use of commercial IT resources worldwide. But, since cloud environments are unsteady and the loading accessible to the cloud is constantly changing and the users require different amounts of resources at different times, such technologies are useful. Moreover, the multi-objective optimization methods have been proposed to meet these challenges by considering the objectives that could compete with each other like costs, energy consumption, and system performance. Consequently, the paper aims at examining the use of MOGAs in the optimization of resources in cloud computing in the dynamic environment. MOGAs mimic the process of natural evolution and thus are good in finding the best compromise between the conflicting objectives in terms of the performance measures. In order to meet these challenges, we present an adaptive MOGA based approach which can make resources by dynamically formulating virtual resources according to the real time loading and wanted quality to be provided to the customers. Applied analysis proves the applicability of the proposed approach to achieve energy efficiency, load balancing, and minimum operational cost. When compared with the conventional heuristic methods, the effectiveness of MOGAs in managing intricate and dynamic environments of clouds has been established. The contribution of this research is helpful to improve the performance of intelligent resource management to help build the sustainable cloud structures.