Abstract
Edge computing is one of the main components of 5 g technology. It is used to adapt to the rapid development of the Internet of things and improve the service quality of the network. The combination of SDN and NFV can improve the flexibility of network service deployment. But at the edge of the network, the change of service will affect the quality of service. Therefore, we need a real-time business change perception system to provide differentiated services to improve the quality of different services. This paper proposes a lightweight service awareness technology based on network edge. This technology is based on the perception of mobile service flow in hierarchical echo state network (ESN). Traffic flow is sensed by discrete echo state network algorithm, and mobile resource scheduling and allocation ability are improved. Finally, the experimental results show that the proposed method can sense the services at the edge of the network, improve the capacity and bandwidth of the network, meet the differentiation of multiple services and QoS.
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
Similar content being viewed by others
References
Hairuman, A., Zahra, A., Kusuma, G.P., Murad, D.F.: MEC deployment with distributed cloud in 4G network for 5G success. In: 2019 6th International Conference on Information Technology, Computer and Electrical Engineering (ICITACEE), pp. 1–6 (2019)
Jiang, D., Wang, Y., Lv, Z., Wang, W., Wang, H.: An energy-efficient networking approach in cloud services for IIoT networks. IEEE J. Sel. Areas Commun. 38(5), 928–941 (2020)
Guerzoni, R., et al.: Network functions virtualisation: an introduction benefits enablers challenges and call for action introductory white paper. SDN and OpenFlow World Congress 1, 5–7 (2012)
He, K., Fisher, A., Wang, L., Gember, A., Akella, A., Ristenpart, T.: Next stop the cloud: understanding modern web service deployment in ec2 and azure. In: Conference on Internet Measurement Conference, pp. 177–190 (2013)
Jiang, D., Wang, W., Shi, L., Song, H.: A compressive sensing-based approach to end-to-end network traffic reconstruction. IEEE Trans. Netw. Sci. Eng. 7(1), 507–519 (2020)
Han, S., Li, J., Ma, Y.X., Dong, Q., et al.: A dynamic energy-saving deployment algorithm for virtual data centers. In: Conference on Smart Cloud, pp. 92–97 (2019)
Jiang, D., Huo, L., Song, H.: Rethinking behaviors and activities of base stations in mobile cellular networks based on big data analysis. IEEE Trans. Netw. Sci. Eng. 7(1), 80–90 (2020)
Wang, S., Zhang, X., Zhang, Y., et al.: A survey on mobile edge networks: convergence of computing caching and communications. IEEE Access 99, 1 (2017)
Zhao, X., Yongchareon, S., Cho, N., Shen, J., Dewan, S.: Enabling intelligent business processes with context awareness. In: 2018 IEEE International Conference on Services Computing (SCC), pp. 153–160 (2018)
Jiang, D., Wang, Y., Lv, Z., Qi, S., Singh, S.: Big data analysis based network behavior insight of cellular networks for industry 4.0 applications. IEEE Trans. Ind. Inf. 16(2), 1310–1320 (2020)
Zhang, L., et al.: Service-aware network slicing supporting delay-sensitive services for 5G fronthaul. In: 2018 23rd Opto-Electronics and Communications Conference (OECC), Jeju Island, Korea (South), pp. 1–2 (2018)
Han, S., Li, J., Dong, Q., Ma, Y., Song, L.: Service-aware based virtual network functions deployment scheme in edge computing. In: International Conference on Advanced Communication Technology (ICACT), pp. 562–565 (2020)
Jiang, D., Huo, L., Lv, Z., Song, H., Qin, W.: A joint multi-criteria utility-based network selection approach for vehicle-to-infrastructure networking. IEEE Trans. Intell. Transp. Syst. 19(10), 3305–3319 (2018)
Yan, Z., Zhao, M., Westphal, C., Chen, C.W.: Toward guaranteed video experience: service-aware downlink resource allocation in mobile edge networks. IEEE Trans. Circ. Syst. Video Technol. 29(6), 1819–1831 (2019)
Bi, Y., Han, G., Lin, C., et al.: Intelligent quality of service aware traffic forwarding for software-defined networking/open shortest path first hybrid industrial internet. IEEE Trans. Industr. Inf. 16(2), 1395–1405 (2020)
Acknowledgements
This work was supported in part by the Science and technology program of State Grid “Research on Key Technologies of New Generation Power Data Communication Network Based on SDN/NFV” (No. 5700-201952237A-0-0-00). The authors wish to thank the reviewers for their helpful comments.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Li, G., Liu, C., Chen, Y., Ma, R., Chen, X., Sun, G. (2021). A Lightweight Network Edge Service-Aware Method for Edge Networks. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 369. Springer, Cham. https://doi.org/10.1007/978-3-030-72792-5_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-72792-5_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-72791-8
Online ISBN: 978-3-030-72792-5
eBook Packages: Computer ScienceComputer Science (R0)