Using Time Discretization to Schedule Scientific Workflows in Multiple Cloud Providers

With the ever-increasing application demands, the execution of applications may require more resources than locally available. In this scenario, resources from multiple IaaS cloud providers can be leased to fulfill application requirements. In this paper we deal with the problem of scheduling workflow applications in multiple IaaS providers, where the workflow scheduler must determine on which computational resource each component of a workflow should be allocated in order to minimize the involved monetary costs. We propose the use of different levels of discrete-time intervals in linear programming to schedule workflows with deadline constraints in multiple cloud providers. Simulations shown that increasing the granularity level of time-discretization decreases the scheduler running time, although yet achieving good solutions.