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Cloud computing aims to give users virtually unlimited pay-per-use computing resources without the burden of managing the underlying infrastructure. The authors present a new job execution environment Flextic that exploits scalable static scheduling techniques to provide the user with a flexible pricing model, such as a trade-off between different degrees of execution speed and execution price, and at the same time, reduce scheduling overhead for the cloud provider. They have evaluated a prototype of Flextic on Amazon EC2 and compared it against Hadoop. For various data parallel jobs from machine learning, image processing, and gene sequencing that they considered, Flextic has low scheduling overhead and reduces job duration by up to 15% compared to Hadoop, a dynamic cloud scheduler.
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