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Cloud Computing is an evolving information technology development, deployment and delivery model consisting of a collection of interconnected and virtualized computers enabling real time delivery of services and solutions over the Internet. One of the critical concerns in this environment is the provisioning of optimal software and hardware resources to ensure a better quality of service (QoS). The classic cloud computing model where services are provided by a single vendor introduces numerous challenges. Cloud services may be interrupted due to unavailability, natural disaster or abrupt increase of the load and hence the system may not be able to provide services to thousands of customers who solely rely and pay for resources. One of the recently emerging areas in cloud computing is deployment of virtual machines across multiple clouds based on providers’ ranking. This involves benchmarking of different cloud providers, development of different techniques for selection of candidate providers, frameworks for ranking cloud providers and monitoring service level agreement (SLA) violations. Most of the existing literature is focused on employing centralized approaches for overall system ranking and monitoring, however, these approaches are not efficient for an environment where job migration and auto-scaling of virtual machines take place across cloud boundaries. The main objective of this research work is the development and evaluation of a QoS based ranking framework for IaaS computing resources across multiple clouds for resource negotiation, provisioning of physical resources, monitoring and ranking, based on job execution experience. We propose a broker enabled QoS ranking, negotiation and monitoring framework based on user level QoS requirements that determine users’ needs and utility for choosing a best-fit cloud provider among a list of candidate cloud providers. Simulation and real test-bed experimentation results suggest that our proposed framework not only gained higher profit margin but also attained more user satisfaction in terms of lower job rejection and failure rate.
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