Abstract
Cloud computing has brought a paradigm shift in computing world. Cloud Service Providers (CSP) use several pricing models for the services they offer. These models can be of short-term or long-term requirements for Cloud Service Users (CSU). Primary objective of our research is to minimize the total cost by finding optimal resource requirements to satisfy CSUs' demand. CSPs like Amazon EC2, a major Infrastructure as a Service (IaaS) provider uses pricing scheme like reserved, on-demand and spot instances for its Virtual Machine (VM) distribution. In this paper, we proposed a QoS aware heuristic approach to minimize job completion time and cost spot instances. In the proposed model, a heuristic approach is used for provisioning spot instances and an artificial neural network (ANN) is employed to predict the pricing spot instance, price which in turn enhances the performance and validate the quality of service. Efficacy of the proposed model is tested using Amazon EC2's real price traces and the total cost of CSUs' are compared with different approaches.
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