An enhanced framework for resource optimization in cloud environment using critical metrics assessment

Abstract

In the current business era, the Cloud computing model provides a pool of resources that are easily accessible, available, scalable and providing and#8213;pay as you useand#8214; provisioning for the clients of different nature. Therefore, the Cloud service providers are outfitted to satisfy clients and thereby attracting more clients and getting more profits. This could be achieved by implementing reliable, effective and efficient resource management approaches. When the client real time requirements dynamically change from time to time at a quick rate, all conventional resource allocation mechanism may fail to meet the client satisfaction. Therefore, this research work presents a new dynamic resource management framework using workflow scheduling called Process Size based Dynamic Scheduling ( PSDS) which maximizes customer satisfaction as well as providers profit. The results demonstrate the efficiency of the PSDS framework and shows it has a better option in terms of newlineincrease in resource utilization, reduced waiting time and reduction in missed process considerably. A multi-objective nature is inherent in Cloud resource scheduling. The objectives of Cloud providers, Cloud users, and other stakeholders can be independent. Resource allocation among multiple clients has to be ensured as newlineper ser vice level agreements. Several techniques have been invented and newlinetested by research community for the development of optimal scheduling in newlineCloud environment. To accomplish these goals and achieve high performance, it is important to design and develop a Responsive Multi- Objective Load Balancing Transformation algorithm (RMOLBT) based on abstraction in multi Cloud environment. newline newline