Journal article

Deadline-constrained coevolutionary genetic algorithm for scientific workflow scheduling in cloud computing

Li Liu, Miao Zhang, Rajkumar Buyya, Qi Fan

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE | WILEY | Published : 2017

Abstract

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 g..

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Grants

Awarded by National Natural Science Foundation of China


Awarded by Beijing Natural Science Foundation


Funding Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant nos. 61370132, 61472033, and 61272432) and Beijing Natural Science Foundation (no. 4152034).