Static scheduling in clouds

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