Budget optimized dynamic virtual machine provisioning in hybrid cloud using fuzzy analytic hierarchy process

Abstract

Among various deployment models, Hybrid cloud is the preferred model allowing customers to maximize cost savings and performance by leveraging advantage of quick provisioning capabilities of public cloud. However, due to vast diversity of cloud services, today’s struggle for the customers is to evaluate and discover the best fit Cloud Service Provider (CSP) for an efficient Virtual Machine (VM) provisioning in various hybrid cloud contracts of multi cloud environment. Currently there is no framework that allows customers to evaluate CSPs based on application workload, application environment and employ an budget optimized VM provisioning with dynamically varying factors while adhering to Service Level Agreement (SLA). Hence, the proposed VM Provisioning framework is a Multi Criteria Decision-Making (MCDM) model with an objective to provision the VMs from the well suited CSP in hybrid cloud. The framework initially derives weights for each decision-making input criteria considered as Quality of Service (QoS) Service Measurement Index (SMI) attributes using extent analysis method of Fuzzy Analytical processing (FHP). It ranks the CSP by aggregating weights of decision makers among the alternatives. The framework then proposes a budget optimized algorithm called BOPVM to provision VMs in the hybrid cloud based on the order of assigned rank to CSPs. The framework is evaluated and implemented on real time workload among the CSPs – Amazon, Azure and Openstack. The evaluation results shows significant provisioning cost savings of 50% with reduced provisioning time by 65% when compared with existing algorithms.