Dynamic resource provisioning for service-based cloud applications: A Bayesian learning approach

Abstract

Deciding the correct extent of resources needed to run the various cloud services is always a challenge. Often in such dynamic environments, there is a tremendous need for accurate predictions and timely decision making methodologies to estimate the future demands within a minimal cost. This brings in a need to elucidate the research divergence for optimal dynamic resource provisioning that predicts the future enumerated resources on the support of application's type. This paper proposes a framework to provision the resources in an optimal way, by combining the concepts of autonomic computing, linear regression and Bayesian learning. The use of Bayesian learning to the proposed model helps in a proactive decision making process and provide a solid theoretical framework to estimate the future predictions using the prior information available. The autonomic resource provisioning framework proposed here is developed using CloudSim toolkit inspired by a cloud layer model. The efficacy of the proposed technique is evaluated using real world workload traces from google followed by the traces from Clarknet. The model is evaluated for various parameters namely – response time, SLA violations, virtual machine usage hours and cost. It is found that the proposed model lowers the overall cost by 31% with the increase in the usage of resources by 12% when compared with the other existing approaches.