resources, i.e., Aneka first decided how many resources, each of which is either a VM in public cloud or a PM in local cloud/grids, used for running tasks and then provisioned the resources from the resource pool, considering that all of the resources are homogeneous.

Besides minimizing cost, a few work focused on minimizing the makespan of scientific applications by cloud bursting. FermiCloud [@FermiCloud] despatched a VM on the PM that has the highest utilization but still have enough resource for the VM in private cloud. Only when all the resources in private cloud are consumed, VM are deployed on a public cloud. A new VM would be launched in a public cloud only when adding the VM can reduce the average job running time. Kailasam et al. [@CBS1; @CBS2] proposed four cloud bursting schedulers whose main ideas are outsourcing a job to a public cloud when the estimated time between now and beginning execution of the job is greater than the estimated time consumed by migrating the job to the public cloud.

These aforementioned work studied on the task and/or resource management with one objective minimizing financial cost for private cloud providers or minimizing the makespan of applications. There 