An autonomic resource provisioning approach for service-based cloud applications: A hybrid approach

Abstract

In cloud computing environment, resources can be dynamically provisioned on deman for cloud services The amount of the resources to be provisioned is determined during runtime according to the workload changes. Deciding the right amount of resources required to run the cloud services is not trivial, and it depends on the current workload of the cloud services. Therefore, it is necessary to predict the future demands to automatically provision resources in order to deal with fluctuating demands of the cloud services. In this paper, we propose a hybrid resource provisioning approach for cloud services that is based on a combination of the concept of the autonomic computing and the reinforcement learning (RL). Also, we present a framework for autonomic resource provisioning which is inspired by the cloud layer model. Finally, we evaluate the effectiveness of our approach under two real world workload traces. The experimental results show that the proposed approach reduces the total cost by up to 50%, and increases the resource utilization by up to 12% compared with the other approaches.