CLC number: TP302
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2014-05-04
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Pawe? Czarnul. Comparison of selected algorithms for scheduling workflow applications with dynamically changing service availability[J]. Journal of Zhejiang University Science C, 2014, 15(6): 401-422.
@article{title="Comparison of selected algorithms for scheduling workflow applications with dynamically changing service availability",
author="Pawe? Czarnul",
journal="Journal of Zhejiang University Science C",
volume="15",
number="6",
pages="401-422",
year="2014",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1300270"
}
%0 Journal Article
%T Comparison of selected algorithms for scheduling workflow applications with dynamically changing service availability
%A Pawe? Czarnul
%J Journal of Zhejiang University SCIENCE C
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%N 6
%P 401-422
%@ 1869-1951
%D 2014
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1300270
TY - JOUR
T1 - Comparison of selected algorithms for scheduling workflow applications with dynamically changing service availability
A1 - Pawe? Czarnul
J0 - Journal of Zhejiang University Science C
VL - 15
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SP - 401
EP - 422
%@ 1869-1951
Y1 - 2014
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1300270
Abstract: This paper compares the quality and execution times of several algorithms for scheduling service based workflow applications with changeable service availability and parameters. A workflow is defined as an acyclic directed graph with nodes corresponding to tasks and edges to dependencies between tasks. For each task, one out of several available services needs to be chosen and scheduled to minimize the workflow execution time and keep the cost of service within the budget. During the execution of a workflow, some services may become unavailable, new ones may appear, and costs and execution times may change with a certain probability. Rescheduling is needed to obtain a better schedule. A solution is proposed on how integer linear programming can be used to solve this problem to obtain optimal solutions for smaller problems or suboptimal solutions for larger ones. It is compared side-by-side with GAIN, divide-and-conquer, and genetic algorithms for various probabilities of service unavailability or change in service parameters. The algorithms are implemented and subsequently tested in a real BeesyCluster environment.
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