Abstract
Mobile edge computing has always been a key issue in the development of the mobile Internet and the Internet of things, how to efficiently schedule tasks has gradually become the focus of mobile edge computing research. Task scheduling problem belongs to the NP-hard optimization problem. Many traditional heuristic algorithms are applied to deal with the task scheduling problem. For improving the problem that ant colony algorithm has slow convergence speed, an ant colony optimization fuzzy clustering algorithm is proposed in this paper. In this algorithm, the fuzzy clustering algorithm is used to reduce the search space range in order to reduce the complexity of the scheduling algorithm and the number of iterations. And the optimal solution of the scheduling is found using the strong global search ability of ant colony algorithm. The simulation results show that the performance of the ant colony optimization fuzzy clustering algorithm is better than that of the First-Come-First-Served algorithm and the traditional ant colony optimization algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mao, Y., You, C., Zhang, J., et al.: A survey on mobile edge computing: the communication perspective. IEEE Commun. Surv. Tutor. 19(4), 2322–2358 (2017)
Abbas, N., Zhang, Y., Taherkordi, A., et al.: Mobile edge computing: a survey. IEEE Internet Things J. PP(99), 1 (2017)
Wei, X., Fan, J., Lu, Z., et al.: Bio-inspired application scheduling algorithm for mobile cloud computing. In: Fourth International Conference on Emerging Intelligent Data and Web Technologies, pp. 690–695. IEEE Computer Society (2013)
Wei, X., Fan, J., Wang, T., et al.: Efficient application scheduling in mobile cloud computing based on MAX—MIN ant system. Soft. Comput. 20(7), 2611–2625 (2016)
Wei, X., Fan, J., Lu, Z., Ding, K.: Application scheduling in mobile cloud computing with load balancing. J. Appl. Math. 2013(3), 337–366 (2013)
Wang, T., Wei, X., Tang, C., et al.: Efficient multi-tasks scheduling algorithm in mobile cloud computing with time constraints. Peer-to-Peer Netw. Appl. 5, 1–15 (2017)
Ravi, A., Peddoju, S.K.: Mobility managed energy efficient Android mobile devices using cloudlet. In: Students’ Technology Symposium, pp. 402–407. IEEE (2014)
Deng, J., Wang, Y., Dong, Z.: Dynamic trajectory pattern mining facing location prediction. Appl. Res. Comput. 34(10), 2984–2988 (2017). (In Chinese)
Wang, T., Liu, Z., Chen, Y., et al.: Load balancing task scheduling based on genetic algorithm in cloud computing. In: Control Conference. IEEE (2016)
Hao-Rong, Z., Ping-Hua, C., Jian-Bin, X., et al.: Task scheduling algorithm based on simulated annealing ant colony algorithm in cloud computing environment. J. Guangdong Univ. Technol. 31, 77–82 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Liu, J., Wei, X., Wang, T., Wang, J. (2019). An Ant Colony Optimization Fuzzy Clustering Task Scheduling Algorithm in Mobile Edge Computing. In: Li, J., Liu, Z., Peng, H. (eds) Security and Privacy in New Computing Environments. SPNCE 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 284. Springer, Cham. https://doi.org/10.1007/978-3-030-21373-2_51
Download citation
DOI: https://doi.org/10.1007/978-3-030-21373-2_51
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-21372-5
Online ISBN: 978-3-030-21373-2
eBook Packages: Computer ScienceComputer Science (R0)