To optimize load of hybrid P2P cloud data-center using efficient load optimization and resource minimization algorithm

Abstract

Cloud is the most trending technology used at almost in every part of the business and in every field of business. Cloud provides number of services in wider spectrum to cloud users from anywhere at any time. But, it achieved through several parameters like deployment model, resource optimization, load optimization etc. Nowadays, Load optimization is playing crucial role in cloud computing behalf system performance. The best optimization technique goal is to fulfill the user requirement efficiently with minimal resources and processing time. Parallel task processing is highly demanded in cloud application. The CPU resources are needed to move for parallelism growth due to communication and synchronization of parallel job arrival in cloud. It is difficult but highly demanded for a data center to response arrived task in parallel way. The objective of work is to design Efficient Load Optimization and Resource Minimization (ELORM) algorithm for optimizing the tasks at different Hybrid P2P Cloud data center zones and different users in cloud environment. The works provides an effective way to distribute the resources based on load prediction in the data centers for resource optimization. It enhances the load optimization, by maintaining the reliability and stability between the user base and data center during data transmission process. It also reduces the resource utilization and response time of the proposed algorithm compared than conventional methods. Proposed ELORM reduces 83.13 s Task completion time, 20.82 $ Virtual machine cost, 6.68% load balancing compare than conventional methods.