Markov Decision Process to Dynamically Adapt Spots Instances Ratio on the Autoscaling of Scientific Workflows in the Cloud
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Spot instances are extensively used to take advantage of large-scale Cloud infrastructures at lower prices than traditional on-demand instances. Autoscaling scientific workflows in the Cloud considering both spot and on-demand instances presents a major challenge as the autoscalers have to determine the proper amount and type of virtual machine instances to acquire, dynamically adjusting the number of instances under each pricing model (spots or on-demand) depending on the workflow needs. Under budget constraints, this adjustment is performed by an assignment policy that determines the suitable proportion of the available budget intended for each model. We propose an approach to derive an adaptive budget assignment policy able to reassign the budget at any point in the workflow execution. Given the inherent variability of the resources in a Cloud, we formalize the described problem as a Markov Decision Process and derive adaptive policies based on other baseline policies. Experiments demonstrate that our policies outperform all the baseline policies in terms of makespan and most of them in terms of cost. These promising results encourage the future study of new strategies aiming to find optimal budget policies applied to the execution of workflows on the Cloud.
This research is supported by the ANPCyT projects No. PICT-2012-2731 and PICT-2014-1430; and by the UNCuyo project No. SeCTyP-M041. The authors want to thank the anonymous reviewers for their valuable comments and suggestions that helped to improve the quality of this paper.
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