Dynamic resource provisioning in IaaS cloud environment

Abstract

Elasticity is one of the key-enablers of cloud systems, minimizing the cost of resource provisioning while meeting critical Quality of Service (QoS) requirements of a service level agreement (SLA). Most internet based services have SLA's that demand stringent performance requirements. Automated resource provisioning (AutoScaling) is an effective way of dealing with workload fluctuations by allocating resources based on the current demand. Simple reactive approaches to AutoScaling can have a contrasting effect on performance, while over-provisioning substantially increases the costs. To tackle these challenges, there is a need for intelligent resource provisioning mechanisms that can model, analyse and predict the resource demand.

This thesis outlines the key practical issues involved in AutoScaling in an Infrastructure as a Service (IaaS) cloud environment and provides tangible solutions. We study a few prediction models and make a comparative analysis on their strengths and weaknesses. We then present the predictive elastic resource controller that addresses the issues in AutoScaling by using modelling techniques from statistical analysis. The research also identifies issues relating to resource demand and capacity estimation in a multi-tenant cloud environment.

A prototype model of the predictive resource controller was implemented on an OpenNebula based cluster. Real world and artificial workload traces were used to test the efficiency of the model. We have also made a comparative analysis of our proposed model with a simple, reactive resource controller. Simulation results show that our model outperforms a simple, reactive resource controller in. terms of prediction error, QoS and number of SLA violations.