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Edge cloud computing service composition based on modified bird swarm optimization in the internet of things

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Abstract

The rapid development of cloud platforms provides a large amount of cloud service resources, which have similar functions and different values of QoS attribute. QoS-based service composition in the edge cloud computing environment faces the main problem that how to combine different cloud services to make global QoS value of service composition to reach the maximization, which is under the premise of meeting the local QoS requirements of edge users. In this paper, the modified bird swarm optimization algorithm is put forward, which introduces the two-order oscillating equation and the historical position memory of the birds on the basis of basic birds swarm optimization. It improves the dynamic parameter mechanism of bird feeding and migration, and enriches the diversity of the birds when moving, and improves the global search ability of the algorithm. By analyzing the results of service combination simulation without local QoS restriction and local QoS restriction, the algorithm can minimize the overall execution time cost of the request under the QoS restriction.

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References

  1. Vandana, C.P., Chikkamannur, A.A.: IOT future in edge computing. Int. J. Adv. Eng. Res. Sci. 3(12), 148–154 (2016)

    Article  Google Scholar 

  2. Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., et al.: iFogSim: a toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software 47(9), 1275–1296 (2017)

    Google Scholar 

  3. Mao, Y., Zhang, J., Letaief, K.B.: Dynamic computation offloading for mobile-edge computing with energy harvesting devices. IEEE J. Sel. Areas Commun. 34(12), 3590–3605 (2016)

    Article  Google Scholar 

  4. Shi, W., Cao, J., Zhang, Q., et al.: Edge computing: vision and challenges. IEEE Internet Things J. , 3(5), 637–646 (2016)

    Article  Google Scholar 

  5. Zhan, S., Huo, H.: Improved PSO-based task scheduling algorithm in cloud computing. J. Inf. Comput. Sci. 9(13), 3821–3829 (2012)

    Google Scholar 

  6. Gu, J., Hu, J., Zhao, T., et al.: A new resource scheduling strategy based on genetic algorithm in cloud computing environment. J. Comput. 7(1), 42–52 (2012)

    Article  Google Scholar 

  7. Jang, S.H., Kim, T.Y., Kim, J.K., et al.: The study of genetic algorithm-based task scheduling for cloud computing. Int. J. Control Autom. 5(4), 157–162 (2012)

    Google Scholar 

  8. Abdullah, M.: Simulated annealing approach to cost-based multi-QoS job scheduling in cloud computing enviroment. Am. J. Appl. Sci. 11(6), 872–877 (2014)

    Article  MathSciNet  Google Scholar 

  9. Fanjiang, Y.-Y., Syu, Y.: Semantic-based automatic service composition with functional and non-functional requirements in design time: a genetic algorithm approach. Inf. Softw. Technol. 56(3), 352–373 (2014)

    Article  Google Scholar 

  10. Mardukhi, F., NematBakhsh, N., Zamanifar, K., et al.: QoS decomposition for service composition using genetic algorithm. Appl. Soft Comput. 13(7), 3409–3421 (2013)

    Article  Google Scholar 

  11. Wu, Q., Zhu, Q.: Transactional and QoS-aware dynamic service composition based on ant colony optimization. Future Gener. Comput. Syst. 29(5), 1112–1119 (2013)

    Article  Google Scholar 

  12. Meng, X.B., Gao, X.Z., Lu, L., et al.: A new bio-inspired optimisation algorithm: Bird Swarm algorithm. J. Exp. Theor. Artif. Intell. 2015(17), 20–22 (2015)

    Google Scholar 

  13. Jian, C.F., Wang, Y.: Batch task scheduling-oriented optimization modelling and simulation in cloud manufacturing. Int. J. Simul. Model. 13(1), 93–101 (2014)

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China under Grant Nos. 61672461 and 61672463.

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Authors and Affiliations

  1. Computer Science and Technology College, Zhejiang University of Technology, Hangzhou, China

    Chengfeng Jian, Miao Li & Xiang Kuang

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  1. Chengfeng Jian
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  2. Miao Li
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  3. Xiang Kuang
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Correspondence to Chengfeng Jian.

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Jian, C., Li, M. & Kuang, X. Edge cloud computing service composition based on modified bird swarm optimization in the internet of things. Cluster Comput 22 (Suppl 4), 8079–8087 (2019). https://doi.org/10.1007/s10586-017-1630-9

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  • DOI: https://doi.org/10.1007/s10586-017-1630-9

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