Adaptive Resource Management in SLA Aware Elastic Clouds

Abstract

In recent years, there has been an increasing interest in solving the over-provisioning and under-provisioning of elastic cloud resources because of the Service Level Agreement (SLA) violation problem. The recent studies have reported that federated cloud services may serve as a better elastic cloud model over a single provider model. A major problem with the federated cloud is the interoperability between multiple cloud service providers. Therefore in this thesis, a proactive SLA aware adaptive resource management approach is proposed for elastic cloud services. Aim of this thesis is to develop a suitable SLA monitoring framework to predict the SLA violations and adaptively allocate the cloud resources to improve the elasticity. It achieves the mutual benefits for cloud consumers and service providers by means of calculating and reducing penalty cost. Our framework has been implemented and validated on a private cloud using OpenNebula 4.0. The results have shown that the proposed proactive approach has significantly reduced the SLA violations compared to a reactive approach. As an additional contribution, the presented work solves the interoperability issues of the federated cloud using an innovative SLA matching algorithm. The simulation results of this work show that the said approach performs better than its counterparts.