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Leading Cloud providers offer several types of Virtual Machines (VMs) indiverse contract models, with different guarantees in terms of availability andreliability. Among them, the most popular contract models are the on-demand andthe spot models. In the former, on-demand VMs are allocated for a fixed costper time unit, and their availability is ensured during the whole execution. Onthe other hand, in the spot market, VMs are offered with a huge discount whencompared to the on-demand VMs, but their availability fluctuates according tothe cloud's current demand that can terminate or hibernate a spot VM at anytime. Furthermore, in order to cope with workload variations, cloud providershave also introduced the concept of burstable VMs which are able to burst uptheir respective baseline CPU performance during a limited period of time withan up to 20% discount when compared to an equivalent non-burstable on-demandVMs. In the current work, we present the Burst Hibernation-Aware DynamicScheduler (Burst-HADS), a framework that schedules and executes tasks ofBag-of-Tasks applications with deadline constraints by exploiting spot andon-demand burstable VMs, aiming at minimizing both the monetary cost and theexecution time. Based on ILS metaheuristics, Burst-HADS defines an initialscheduling map of tasks to VMs which can then be dynamically altered bymigrating tasks of a hibernated spot VM or by performing work-stealing when VMsbecome idle. Performance results on Amazon EC2 cloud with differentapplications show that, when compared to a solution that uses only regularon-demand instances, Burst-HADS reduces the monetary cost of the execution andmeet the application deadline even in scenarios with high spot hibernationrates. It also reduces the total execution time when compared to a solutionthat uses only spot and non-burstable on-demand instances.

Scheduling Bag-of-Tasks in Clouds using Spot and Burstable Virtual Machines