Abstract
This paper investigates various techniques for scheduling the costs of parameter sweep calculations efficiently in a hybrid cloud environment. The combination of both a private and a public cloud environment integrates the advantages of being cost effective and having virtually unlimited scaling capabilities at the same time. To make an accurate estimate for the required resources, multiple prediction techniques are discussed. The estimation can be used to create an efficient scheduler which respects both deadline and cost. These findings have been implemented and tested in a Java-based cloud framework which operates on Amazon EC2 and OpenNebula. Also, we present a theoretical model to further optimise the cost by leveraging the Amazon Spot Market.
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