An efficient load balancing and resource provisioning in cloud computing

Abstract

Cloud computing is incredibly significant in recent technologies in IT newlinesector. There are various types of cloud computing services and applications newlineavailable via the internet connection. Cloud computing makes it possible to newlinehave platforms such as these more reliably and cost-effectively are IaaS, SaaS, newlinePaaS, to the service requesters. As cloud computing is serving millions of users newlinesimultaneously, it must have the ability to meet all users requests with high newlineperformance and guarantee of quality of service (QoS). Therefore, we need to newlineimplement an appropriate scheduling algorithm to efficiently meet those newlinerequests. Scheduling problem is the one of the most critical issues in cloud newlinecomputing environment because cloud performance depends mainly on it. newlineThe scheduling algorithm has the advantage of regulating energy newlineconsumption through workload allocation. Various kinds of scheduling newlinealgorithm are implemented to lessen the execution time but the ultimate issue newlineenergy consumption not yet considered. Energy aware scheduling algorithm is newlineconcentrated on both makespan and also in energy consumption. In this work a newlinenovel scheduling algorithm based on the factors workload and job type to newlinepredict the makespan and also energy consumption. The motivation of this newlinescheduling algorithm is to achieve the energy efficient green task scheduling newlineand to optimize the scheduler that uses the sigmoid neural task predictor for the newlineimplementation. newlineResource provisioning in cloud computing is a major component that newlinecan improve the performance of a cloud system to a huge extent. High newlinedimensionality and high variability in the cloud workloads pose major newlinechallenges in the allocation process. This work presents an architecture that newlineperforms resource provisioning based on demand prediction and range-based newlineresource allocation that ensures reduced reallocation. newline