Abstract Maximizing resource utilization by performing an efficient resource provisioning is a key factor for any cloud provider: commercial actors can maximize their revenues, whereas scientific and non-commercial providers can maximize their infrastructure utilization. Traditionally, batch systems have allowed data centers to fill their resources as much as possible by using backfilling and similar techniques. However, in an IaaS cloud, where virtual machines are supposed to live indefinitely, or at least as long as the user is able to pay for them, these policies are not easily implementable. In this work we present a new scheduling algorithm for IaaS providers that is able to support preemptible instances, that can be stopped by higher priority requests without introducing large modifications in the current cloud schedulers. This scheduler enables the implementation of new cloud usage and payment models that allow more efficient usage of the resources and potential new revenue sources for commercial providers. We also study the correctness and the performance overhead of the proposed scheduler against existing solutions.
Besides simplistic scheduling policies like first-fit or random chance node selection @cite_33 , current CMF implement a scheduling algorithm that is based on a rank selection of hosts, as we will explain in what follows:
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The typical batch queuing system schedules jobs for execution by a set of queue controls. The controls determine the queue from which jobs will be selected. Within each queue, jobs are typically selected in first-in, first-out (FIFO) order. This limits the set of scheduling policies available to a site.