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We propose throughput and cost optimal job scheduling algorithms in cloudcomputing platforms offering Infrastructure as a Service. We first consideronline migration and propose job scheduling algorithms to minimize jobmigration and server running costs. We consider algorithms that assumeknowledge of job-size on arrival of jobs. We characterize the optimal costsubject to system stability. We develop a drift-plus-penalty framework basedalgorithm that can achieve optimal cost arbitrarily closely. Specifically thisalgorithm yields a trade-off between delay and costs. We then relax thejob-size knowledge assumption and give an algorithm that uses readily offeredservice to the jobs. We show that this algorithm gives order-wise identicalcost as the job size based algorithm. Later, we consider offline job migrationthat incurs migration delays. We again present throughput optimal algorithmsthat minimize server running cost. We illustrate the performance of theproposed algorithms and compare these to the existing algorithms viasimulation.

Scheduling Policies for Stability and Optimal Server Running Cost in Cloud Computing Platforms