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Learning budget assignment policies for autoscaling scientific workflows in the cloud

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Abstract

Autoscalers exploit cloud-computing elasticity to cope with the dynamic computational demands of scientific workflows. Autoscalers constantly acquire or terminate virtual machines (VMs) on-the-fly to execute workflows minimizing makespan and economic cost. One key problem of workflow autoscaling under budget constraints (i.e. with a maximum limit in cost) is determining the right proportion between: (a) expensive but reliable VMs called on-demand instances, and (b) cheaper but subject-to-failure VMs called spot instances. Spot instances can potentially provide huge parallelism possibilities at low costs but they must be used wisely as they can fail unexpectedly hindering makespan. Given the unpredictability of failures and the inherent performance variability of clouds, designing a policy for assigning the budget for each kind of instance is not a trivial task. For such reason we formalize the described problem as a Markov decision process that allows us learning near-optimal policies from the experience of other baseline policies. Experiments over four well-known scientific workflows, demonstrate that learned policies outperform the baseline policies considering the aggregated relative percentage difference of makespan and execution cost. These promising results encourage the future study of new strategies aiming to find optimal budget policies applied to the execution of workflows in the cloud.

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Notes

  1. Note that knowing the exact task running-times and the future spot prices progression would permit the design of an optimal policy able to minimize makespan and cost. However, under real cloud execution conditions such assumptions are not realistic and therefore they are not considered in this work.

  2. Southern California Earthquake Center: http://www.scec.org

  3. IPAC: http://www.ipac.caltech.edu/

  4. NASA: http://www.nasa.gov/

  5. LIGO:http://www.ligo.caltech.edu/

  6. NCBI:https://www.ncbi.nlm.nih.gov/

  7. http://confluence.pegasus.isi.edu/display/pegasus/WorkflowGenerator

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Acknowledgements

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 first author acknowledges her PhD fellowship granted by the CONICET.

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Authors and Affiliations

  1. ITIC-CONICET, Universidad Nacional de Cuyo (UNCuyo), Mendoza, Argentina

    Yisel Garí

  2. ITIC, Universidad Nacional de Cuyo (UNCuyo), Mendoza, Argentina

    David A. Monge & Carlos García Garino

  3. ISISTAN-CONICET, UNICEN, Tandil, Buenos Aires, Argentina

    Cristian Mateos

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  1. Yisel Garí
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  2. David A. Monge
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  4. Carlos García Garino
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Correspondence to David A. Monge.

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Garí, Y., Monge, D.A., Mateos, C. et al. Learning budget assignment policies for autoscaling scientific workflows in the cloud. Cluster Comput 23, 87–105 (2020). https://doi.org/10.1007/s10586-018-02902-0

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