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Fig. 4. Execution time of each algorithm
5
Conclusions
In this paper, we analyzed the workflow scheduling problem on computational grids.
We proposed bacterial foraging based algorithm to solve the problem. We used an
intermediate representation for the scheduling solution. The proposed approach is to
generate an optimal schedule so as to complete the workflow in minimum period of
time. We evaluated the performance of bacterial foraging algorithm and compared the
performance with ant colony algorithm and particle swarm optimization. BFO based
method outperforms other algorithm in minimizing the makespan and it takes less
execution time to get the optimal solution when the workflow task size becomes
large.
Acknowledgments. This work was supported by the National Natural Science
Foundation, China (No.61100039, 61021062, 61272188, 91318301), the 973 Program
(2009CB320702), the Natural Science Foundation of Jiangsu Province
(No.BK20131277), the Fund of State Key Laboratory for Novel Software Technology
(Nanjing University), the Open Foundation of State key Laboratory of Networking
and Switching Technology (Beijing University of Posts and Telecommunications)
(SKLNST-2013-1-14).
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