Grouping Based Load Balancing in Cloud Computing

Cloud Computing is a new and inevitable technology in the fields of scientific, and engineering, and as well as in commercial, and industrial enterprises. In cloud computing there are many tasks that needs to be executed by the available resources to acquire high performance, reduce task completion time, minimize response time, utilization of resource usage and etc. However, user tasks developed for cloud might be small and of varying lengths according to their computational needs and other requirements. Process Migration is a technique whereby an active process is moved from one machine to another of possibly different architecture. This necessitates the capture of the process’s current state of execution and recovering it on the destination machine in a manner understandable to it. My work mainly focuses on capture and recovery of the internal state of process, comprising of the execution and data state – activation history, and static and heap challenge to design an efficient scheduling strategy to achieve high performance in cloud computing. There are many existing algorithms for task scheduling but not reducing communication overhead time and computation time, and on the other hand maximizing resource utilization. The purpose of the study is to analyze and achieve better performance by taking new concept of grouping based task scheduling Therefore, this paper proposes “New Task Scheduling Grouping Base Model” with the objective of minimizing overhead time and computation time, thus reducing overall processing time of tasks.