Abstract
Task scheduling is one of the basic problem on cloud computing. In hybrid cloud, tasks scheduling faces new challenges. In order to better deal the multi-objective task scheduling optimization in hybrid clouds, on the basis of the GaDE and Pareto optimum of quick sorting method, we present a multi-objective algorithm, named NSjDE. This algorithm also makes considerations to reduce the frequency of evaluation Comparing with experiment of Min-Min algorithm, GaDE algorithm and NSjDE algorithm, results show that for the single object task scheduling, GaDE and NsjDE algorithms perform better in getting the approximate optimal solution. The optimization speed of multi-objective NSjDE algorithm is faster than the single-objective jDE algorithm, and NSjDE can produce more than one non-dominated solution meeting the requirements, in order to provide more options to the user.
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References
Goudarzi, H., Ghasemazar, M., Pedram, M.: Sla-based optimization of power and migration cost in cloud computing. In: 2012 Conference Proceedings on 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp. 172–179. IEEE (2012)
Kumar, B.A., Ravichandran, T.: Time and cost optimization algorithm for scheduling multiple workflows in hybrid clouds. Eur. J. Sci. Res. 89(2), 265–275 (2012)
Xue, S.-J., Wu, W.: Scheduling workflow in cloud computing based on hybrid particle swarm algorithm. TELKOMNIKA Indonesian J. Electr. Eng. 10(7), 1560–1566 (2012)
Xu, X., Hu, N., Ying, W.Q.: Cloud task and virtual machine allocation strategy based on simulated annealing-genetic algorithm. Appl. Mech. Mater. 513, 391–394 (2014)
Sellami, K., Ahmed-Nacer, M., Tiako, P.F., Chelouah, R.: Immune genetic algorithm for scheduling service workflows with qos constraints in cloud computing. S. Afr. J. Ind. Eng. 24(3), 68–82 (2013)
Yassa, S., Sublime, J., Chelouah, R., Kadima, H., Jo, G., Granado, B.: A genetic algorithm for multicobjective optimisation in workflow scheduling with hard constraints. Int. J. Metaheuristics 2(4), 415–433 (2013)
Liu, W., Du, W., Chen, J., Wang, W., Zeng, G.: Adaptive energy-efficient scheduling algorithm for parallel tasks on homogeneous clusters. J. Netw. Comput. Appl. 41, 101–113 (2013)
Liu, J., Luo, X.-G., Zhang, X.-M., Zhang, F., Li, B.-N.: Job scheduling model for cloud computing based on multi-objective genetic algorithm. Int. J. Comput. Sci. Issues (IJCSI) 10(1), 134–139 (2013)
Acknowledgment
The work was partially supported by Project 61501412 supported by National Natural Science Foundation of China.
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Fan, Y. et al. (2016). Executing Time and Cost-Aware Task Scheduling in Hybrid Cloud Using a Modified DE Algorithm. In: Li, K., Li, J., Liu, Y., Castiglione, A. (eds) Computational Intelligence and Intelligent Systems. ISICA 2015. Communications in Computer and Information Science, vol 575. Springer, Singapore. https://doi.org/10.1007/978-981-10-0356-1_8
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DOI: https://doi.org/10.1007/978-981-10-0356-1_8
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