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Duplication Scheduling with Bottom-Up Top-Down Recursive Neural Network

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Intelligent Data Engineering and Automated Learning – IDEAL 2022 (IDEAL 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13756))

Abstract

Scientific workflows can be represented as directed acyclic graphs (DAGs) with nodes corresponding to individual tasks and directed edges between the nodes signifying the order of task execution. The nodes contain informative attributes related to task-specific data transfer/storage and scheduling length. Given an available amount of (cloud) computational resources, the overall workflow scheduling length can sometimes be reduced by making certain “critical tasks” run in parallel (task duplication) on multiple resources. In this way, a carefully designed spread of computational effort across the resources can result in a more efficient computational structure and hence a shorter scheduling length. However, task duplication algorithms deciding which tasks to duplicate in a given workflow can themselves be computationally expensive. Here we propose a novel Bottom-Up Top-Down Recursive Neural Network (BUTD RecNN) model that is able to learn from historical duplication decisions on workflows (represented as DAGs) to efficiently produce duplication recommendations for new unseen workflows. The approach is tested on collections of Montage workflows.

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Notes

  1. 1.

    The number of recursive connections of a recursive neuron should be equal to the \(max\_degree\) of the domain D, even if not all of them will be used for computing the output of a vertex v with \(out\_deg(v)< max\_degree\).

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Authors and Affiliations

  1. School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK

    Vahab Samandi, Peter Tiňo & Rami Bahsoon

Authors
  1. Vahab Samandi
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  2. Peter Tiňo
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  3. Rami Bahsoon
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Correspondence to Vahab Samandi .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Hujun Yin

  2. Technical University of Madrid, Madrid, Spain

    David Camacho

  3. University of Birmingham, Birmingham, UK

    Peter Tino

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Samandi, V., Tiňo, P., Bahsoon, R. (2022). Duplication Scheduling with Bottom-Up Top-Down Recursive Neural Network. In: Yin, H., Camacho, D., Tino, P. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2022. IDEAL 2022. Lecture Notes in Computer Science, vol 13756. Springer, Cham. https://doi.org/10.1007/978-3-031-21753-1_17

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  • DOI: https://doi.org/10.1007/978-3-031-21753-1_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21752-4

  • Online ISBN: 978-3-031-21753-1

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