Hybrid Genetic Algorithm based Proficient Resource Management and Scheduling in Grid Computing

Abstract

Grid networking is an aggregation of geographically dispersed computing, storage, and network resources, coordinated to deliver improved performance, higher quality of service, better utilization, and easier access to data. It enables virtual, collaborative organizations, sharing applications and data in an open, heterogeneous environment. Scheduling is the process that selects which job in the queue should be considered next. Grid Scheduling is the process of making scheduling decisions involving allocating jobs to resources over multiple administrative domains. The goal of scheduling is to minimize the make-span by finding an optimal solution. In the present Grid Networking environment, the scheduling approaches for VM (Virtual Machine) resources only focus on the current state of the entire system. Most often they fail to consider the system variation and historical behavioral data which causes system load imbalance. In existing system is based only on future load prediction mechanism. Based on this factor VM resource allocation is done. During this VM migration, there is no suitable criterion for unique identification and location of VM that means which VM is migrated and where to be migrated. In this paper a Grid Booster Algorithm is using. In this system VM allocation is based on resource weight [a value indicates capacity of each resource]. Based on these weights a VM resource allocation mechanism has proposed, which is considering both resource weight and future prediction. To produce a better approach for solving the problem ofVM resource migration in a Grid Networking environment, this paper demonstrates Acclimatize Genetic Algorithm based VM resource migration strategy that focuses on system load balancing.