BoT jobs are often composed of hundreds of thousands of independent tasks and are CPU-intensive.

In this paper, we model the task and resource managements of hybrid clouds into a binary nonlinear programming (BNP) model. In this model, the utilization of local resources is maximized while the cost for the resources leased from the public cloud is minimized. As BNP is NP-hard problem [@NIP], we propose a heuristic algorithm to solve this BNP problem. In brief, the contributions of this paper can be summarized as follows:

1.  We model the hybrid cloud management which maximizes the utilization of local resources and minimizes the cost for renting the resources of the public cloud for BoT jobs with deadline constraints into a BNP problem.

2.  To solve the BNP problem in polynomial time, we propose a heuristic algorithm. The algorithm assigns a task to a core such that the difference between the task’s finish time and its deadline is minimum in all assignments between unassigned tasks and cores of used PMs. If there is no such assignment, which means that there is no task can be completed within the deadline by the resources already used, the algorithm adds an available PM with 