INTEGRATED EXTREMAL OPTIMIZATION AND RANDOM FOREST BASED SCHEDULING FOR CLOUD COMPUTING ENVIRONMENT

Abstract

Cloud computing is the most recent continuation of parallel computing, distributed computing and grid computing. Basically it is a parallel and distributed system containing various powerfully interconnected virtualized machines. Virtual machines are assigned over system to give administrations to users. The major problem nowadays occurs in cloud computing is Load Balancing. Basically it is a term used to spread jobs tasks over multiple networks. This paper defines the parallel processor scheduling technique in cloud computing environment. The proposed technique of Random Forest with integration of Extremal Optimization improves the efficiency and results of the tasks spreaded into multiple networks defined by the parameters like speedup, makespan time, migration cost etc. which indicates better outcomes of proposed technique than the existing one.