not apply to a private cloud with heterogeneous PMs. For completing the job on time and with minimum cost, Chu and Simmhan [@reusable] first modelled a time-vary spot VM price as a Markov chain and then established a reusable table with three-tuple elements consisted of job compute requirement, deadline constraint of the job and a series of actions with minimal cost based on the price model. The subsequent action was decided by performing a simple table look-up. In this work, they considered that utilizing off local machines does not incur expense.

These above work studied on either resource provisioning or task scheduling. Moreover, most approaches for dynamic provisioning operate in a per-job level, and thus they are inefficient because they fail in consider that other tasks could utilize idle cycles of cloud resources. To address these problems, Aneka [@Aneka1; @Aneka2; @Aneka3] coordinated dynamic provisioning and scheduling that is able to cost-effectively complete applications within their respective deadlines by considering the whole organization workload at individual tasks level when making decisions and an accounting mechanism to determine the share of the cost of utilization of public cloud resources to be assigned to each user. While Aneka separately scheduled tasks and provisioned 