OPTIMAL WHALE OPTIMIZATION ALGORITHM BASED ENERGY EFFICIENT RESOURCE ALLOCATION IN CLOUD COMPUTING ENVIRONMENT

In cloud, proper allocation of resources improves the exploitation of resources as well as energy efficiency, provider's profit and client's fulfilment. Whale Optimization Algorithm (WOA) is a new bio-inspired meta-heuristic technique inspired from the social hunting nature of humpback whales. WOA suffers premature convergence that causes it to trap in local optima. To resolve it, this paper proposes a new energy efficient resource allocation in cloud computing environment using optimal whale optimization algorithm with tumbling effective called WOA-TRA model. Here, the WOA is hybridized with tumbling effect which has good exploration ability for function optimization problems to derive an energy aware solution. The proposed WOA-TRA model methodology attempts to optimize allocation of resources for improving the energy efficiency of the cloud platform by fulfilling the quality of service (QoS) requirements of the end user. To effectively utilize the energy and QoS requirements, the WOA-TRA technique is utilized in two levels. In the first level, WOA-TRA technique assigns Virtual Machines (VMs) resources to jobs, whereas in second level, WOA-TRA technique assigns Physical Machines (PMs) resources to VMs. The presented model is simulated in CloudSim platform and a detailed comparative analysis is made with the state of art resource allocation techniques. The experimental analysis states that the presented WOA-TRA technique offered desired QoS and enhanced energy efficiency by effectively utilizing the available resources over the compared methods.