Optimizing Edge-Cloud Cooperation for Machine Learning Accuracy Considering Transmission Latency and Bandwidth Congestion
Author:
Affiliation:
1. NTT Network Service and Systems Laboratories, NTT Corporation
2. Faculty of Information Networking for Innovation and Design, Toyo University
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Software
Link
https://www.jstage.jst.go.jp/article/transcom/E106.B/9/E106.B_2022EBP3171/_pdf
Reference27 articles.
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2. [2] R. Boutaba, M.A. Salahuddin, N. Limam, S. Ayoubi, N. Shahriar, F. Estrada-Solano, and O.M. Caicedo, “A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities,” J. Internet Serv. Appl., vol.9, no.1, pp.1-99, 2018. 10.1186/s13174-018-0087-2
3. [3] V. Carela-Español, P. Barlet-Ros, O. Mula-Valls, and J. Solé-Pareta, “An autonomic traffic classification system for network operation and management,” J. Netw. Syst. Manage., vol.23, no.3, pp.401-419, 2015. 10.1007/s10922-013-9293-1
4. [4] M. Wang, Y. Cui, X. Wang, S. Xiao, and J. Jiang, “Machine learning for networking: Workflow, advances and opportunities,” IEEE Netw., vol.32, no.2, pp.92-99, 2017. 10.1109/mnet.2017.1700200
5. [5] L. Kleinrock, Theory, Volume 1, Queueing Systems, Wiley-Interscience, USA, 1975.
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