Waterwheel Plant Algorithm Optimization Based Resource Allocation in the Cloud Computing

In recent years, the Cloud Computing (CC) is one of the most fast developing distributed computing technologies and the cloud-based applications have extended crucially. The CC environment provides the computing resources according to the need of cloud user’s demand. The CC develops resource allocation system by the distributed computing as well as virtualization to highlights the cloud services scalability. One of the most important problems faced by the cloud customers are choosing the resources for the application deployment without compromising the Quality of Service (QoS) requirements. However, to control the user demand creates critical problems in the on-demand resource allocation framework. To address this problem, this research proposed the Waterwheel Plant Algorithm (WWPA) based resource allocation in the cloud. The virtualization conception supports to implements the task based on the resource accessibility as well as minimizes response time. The WWPA utilizes the hunting strategy to attains the efficient and effective resource allocation. The proposed WWPA method attains better results and it achieves the execution time of 80.5667, energy consumption of 7453.201 and resource utilization of 0.876 respectively when compared to the existing methods like Whale Optimization Algorithm (WOA) and Emperor Penguin Optimization (EPO).