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Deadline‐constrained coevolutionary genetic algorithm for scientific workflow scheduling in cloud computing
Author(s) -
Liu Li,
Zhang Miao,
Buyya Rajkumar,
Fan Qi
Publication year - 2016
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.3942
Subject(s) - computer science , cloud computing , workflow , crossover , scheduling (production processes) , distributed computing , genetic algorithm , mathematical optimization , convergence (economics) , artificial intelligence , machine learning , database , mathematics , economics , economic growth , operating system
Summary The cloud infrastructures provide a suitable environment for the execution of large‐scale scientific workflow application. However, it raises new challenges to efficiently allocate resources for the workflow application and also to meet the user's quality of service requirements. In this paper, we propose an adaptive penalty function for the strict constraints compared with other genetic algorithms. Moreover, the coevolution approach is used to adjust the crossover and mutation probability, which is able to accelerate the convergence and prevent the prematurity. We also compare our algorithm with baselines such as Random, particle swarm optimization, Heterogeneous Earliest Finish Time, and genetic algorithm in a WorkflowSim simulator on 4 representative scientific workflows. The results show that it performs better than the other state‐of‐the‐art algorithms in the criterion of both the deadline‐constraint meeting probability and the total execution cost.