Abstract
Cloud Computing is an innovative model that is changing the industry procedure of inventing, developing, deploying, order, updating, maintaining, and budget for applications and the stock on which they run. It is a technique to increase the resource facility or proficiencies without financing in a new hardware or licensing new software. Due to its attractive features, organizations and individuals are getting involved in cloud technology paradigms. However, there are still a few obstacles in total deployment of cloud technology commercially, which is a challenging task. The complexness of the scheduling job, the resource parameter should be augmented by decreasing the execution time. Provisioning the resource and mapping the workload is a crucial role in improving the performance of the complete system. Mapping/Executing the cloudlet should be customer satisfaction and economical to the provider. Cloud technology is facing different challenging issues for allocating the job in an efficient manner to requested user in the peak time. This thesis is composed of most important works like Provisioning and scheduling the required job, RFD metaheuristics algorithm, Performance metrics calculation for response time, execution time, and cost. The current research investigation is the job Scheduling problem of cloud scheduling paradigm and to propose a new approach for provisioning and allocating the resources using RFD algorithm-based technique. The aim of implementing the RFD algorithm in the cloud is to efficiently allocate the resources and better utilization of cloud resources. The proposed effective approach of resource provisioning and scheduling algorithm is to solve the provision of resource and to execute workload efficiently in the cloud paradigm. The theme of research contains the proposed River formation dynamics algorithm which gives a better optimal solution for allocating the resources in the cloud in Comparing with “Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO).” The implementation results show decreasing response time, cost, and execution time.
Published Version
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