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Prediction-based auto-scaling of scientific workflows

The work

AuthorsReginald Cushing; Spiros Koulouzis; Adam S. Z. Belloum; Marian Bubak
Editors
Typemisc
Year2011
Citekeycushing2011predictionbased

Where it appeared

Published inMiddleware for Grid Computing
Pages1--6

Identifiers

DOI10.1145/2089002.2089003
OpenAlexW2027541616

Access

Landing pagehttps://doi.org/10.1145/2089002.2089003

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Abstract

In this paper we propose a novel method for auto-scaling data-centric workflow tasks. Scaling is achieved through a prediction mechanism where the input data load on each task within a workflow is used to compute the estimated task execution time. Through load prediction, the framework can take informed decisions on scaling multiple workflow tasks independently to improve overall throughput and reduce workflow bottlenecks. This method was implemented in the WS-VLAM workflow system and with an image analyses workflow we show that this technique achieves faster data processing rates and reduces overall workflow makespan.

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Added2026-10-29 05:02 UTC
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Cite it as

@misc{cushing2011predictionbased,
  title        = {Prediction-based auto-scaling of scientific workflows},
  author       = {Reginald Cushing and Spiros Koulouzis and Adam S. Z. Belloum and Marian Bubak},
  year         = {2011},
  journal      = {Middleware for Grid Computing},
  pages        = {1--6},
  doi          = {10.1145/2089002.2089003},
}

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