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New approach to allocation planning of many‐task workflows on clouds
Author(s) -
Gerhards Michael,
Sander Volker,
Živković Miroslav,
Belloum Adam,
Bubak Marian
Publication year - 2019
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5404
Subject(s) - computer science , workflow , benchmark (surveying) , task (project management) , cloud computing , schedule , distributed computing , resource allocation , plan (archaeology) , resource (disambiguation) , a priori and a posteriori , real time computing , database , operating system , systems engineering , computer network , geodesy , archaeology , engineering , history , geography , philosophy , epistemology
Summary 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.