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Driven by the need to provision resources on demand,
scientists are turning to commercial and research test-bed
Cloud computing resources to run their scientific experiments.
Job scheduling on cloud computing resources, unlike earlier platforms,
is a balance between throughput and cost of executions.
Within this context, we posit that usage patterns can improve the
job execution, because these patterns allow a system to plan, stage
and optimize scheduling decisions. This paper introduces a novel
approach to utilization of user patterns drawn from knowledgebased
techniques, to improve execution across a series of active
workflows and jobs in cloud computing environments. Using
empirical analysis we establish the accuracy of our prediction
approach for two different workloads and demonstrate how this
knowledge can be used to improve job executions.