Execution time estimation for workflow scheduling

Estimation of the execution time is an important part of the workflow scheduling problem. The aim of this paper is to highlight common problems in estimating the workflow execution time and propose a solution that takes into account the complexity and the randomness of the workflow components and th...

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Veröffentlicht in:2014 9th Workshop on Workflows in Support of Large-Scale Science S. 1 - 10
Hauptverfasser: Chirkin, Artem M., Belloum, A. S. Z., Kovalchuk, Sergey V., Makkes, Marc X.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: Piscataway, NJ, USA IEEE Press 16.11.2014
IEEE
Schriftenreihe:ACM Conferences
Schlagworte:
Chebyshev approximation
Data models
Estimation
Random variables
Runtime
Scheduling
Stochastic processes
Theory of computation
Theory of computation > Theory and algorithms for application domains
Theory of computation > Theory and algorithms for application domains > Machine learning theory
Theory of computation > Theory and algorithms for application domains > Machine learning theory > Reinforcement learning
Theory of computation > Theory and algorithms for application domains > Machine learning theory > Reinforcement learning > Sequential decision making
ISBN:1479970670, 9781479970674
Online-Zugang:Volltext
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Zusammenfassung:Estimation of the execution time is an important part of the workflow scheduling problem. The aim of this paper is to highlight common problems in estimating the workflow execution time and propose a solution that takes into account the complexity and the randomness of the workflow components and their runtime. The solution proposed in this paper addresses the problems at different levels from task to workflow, including the error measurement and the theory behind the estimation algorithm. The proposed estimation algorithm can be integrated easily into a wide class of schedulers as a separate module. We use a dual stochastic representation, characteristic/distribution functions, in order to combine tasks' estimates into the overall workflow makespan. Additionally, we propose the workflow reductions - the operations on a workflow graph that do not decrease the accuracy of the estimates, but simplify the graph structure, hence increasing the performance of the algorithm.
ISBN:1479970670
9781479970674
DOI:10.1109/WORKS.2014.11