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New approach to allocation planning of many‐task workflows on clouds

  • Experience has shown that a priori created static resource allocation plans are vulnerable to runtime deviations and hence often become uneconomic or highly exceed a predefined soft deadline. The assumption of constant task execution times during allocation planning is even more unlikely in a cloud environment where virtualized resources vary in performance. Revising the initially created resource allocation plan at runtime allows the scheduler to react on deviations between planning and execution. Such an adaptive rescheduling of a many-task application workflow is only feasible, when the planning time can be handled efficiently at runtime. In this paper, we present the static low-complexity resource allocation planning algorithm (LCP) applicable to efficiently schedule many-task scientific application workflows on cloud resources of different capabilities. The benefits of the presented algorithm are benchmarked against alternative approaches. The benchmark results show that LCP is not only able to compete against higher complexity algorithms in terms of planned costs and planned makespan but also outperforms them significantly by magnitudes of 2 to 160 in terms of required planning time. Hence, LCP is superior in terms of practical usability where low planning time is essential such as in our targeted online rescheduling scenario.

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Metadaten
Author:Michael Gerhards, Volker Sander, Miroslav Zivkovic, Adam Belloum, Marian Bubak
DOI:https://doi.org/10.1002/cpe.5404
ISSN:1532-0634
Parent Title (English):Concurrency and Computation: Practice and Experience
Publisher:Wiley
Place of publication:Chichester
Document Type:Article
Language:English
Year of Completion:2020
Date of the Publication (Server):2019/07/18
Volume:32
Issue:2 Article e5404
First Page:1
Last Page:16
Link:https://doi.org/10.1002/cpe.5404
Zugriffsart:campus
Institutes:FH Aachen / Fachbereich Medizintechnik und Technomathematik
collections:Verlag / Wiley