A Survey of Machine Learning applied to Computer Networks

We review the current state of the art in the domain of machine learning applied to computer networks. First of all, we describe recent developments in computer networking and outline the potential fields for machine learning that arise from these developments. We discuss challenges for machine learning in this particular field, namely the inherent big data aspect of computer networks, and the fact that learning very often needs to be conducted in a streaming setting with non-stationary data distributions. We discuss practical issues like privacy protection and computing resources before finally outlining potential technological benefits of this emerging scientific field.

[1]  Xin Wang,et al.  Machine Learning for Networking: Workflow, Advances and Opportunities , 2017, IEEE Network.

[2]  Grzegorz Rzym,et al.  Flow Length and Size Distributions in Campus Internet Traffic , 2018, Comput. Commun..

[3]  Miguel Rio,et al.  Deep Neural Networks for Network Routing , 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

[4]  Andreas Blenk,et al.  Adversarial Network Algorithm Benchmarking , 2019, CoNEXT Companion.

[5]  Alexander Clemm,et al.  Network Management Fundamentals , 2006 .

[6]  Guy Pujolle,et al.  NeuRoute: Predictive dynamic routing for software-defined networks , 2017, 2017 13th International Conference on Network and Service Management (CNSM).

[7]  Yee Whye Teh,et al.  Progress & Compress: A scalable framework for continual learning , 2018, ICML.

[8]  Haipeng Yao,et al.  Intention Based Networking Management , 2019, Wireless Networks.

[9]  Zhitang Chen,et al.  Online flow size prediction for improved network routing , 2016, 2016 IEEE 24th International Conference on Network Protocols (ICNP).

[10]  Min Luo,et al.  A Framework for QoS-aware Traffic Classification Using Semi-supervised Machine Learning in SDNs , 2016, 2016 IEEE International Conference on Services Computing (SCC).

[11]  Shervin Shirmohammadi,et al.  Machine Learning and Deep Learning Based Traffic Classification and Prediction in Software Defined Networking , 2019, 2019 IEEE International Symposium on Measurements & Networking (M&N).

[12]  Benedikt Pfülb,et al.  A comprehensive, application-oriented study of catastrophic forgetting in DNNs , 2019, ICLR.

[13]  Nick McKeown,et al.  OpenFlow: enabling innovation in campus networks , 2008, CCRV.

[14]  Stefan Wermter,et al.  Continual Lifelong Learning with Neural Networks: A Review , 2019, Neural Networks.

[15]  Xu Jia,et al.  Continual learning: A comparative study on how to defy forgetting in classification tasks , 2019, ArXiv.

[16]  Marc Peter Deisenroth,et al.  Deep Reinforcement Learning: A Brief Survey , 2017, IEEE Signal Processing Magazine.

[17]  Christoph Hardegen,et al.  Flow-based Throughput Prediction using Deep Learning and Real-World Network Traffic , 2019, 2019 15th International Conference on Network and Service Management (CNSM).

[18]  Fernando M. V. Ramos,et al.  Software-Defined Networking: A Comprehensive Survey , 2014, Proceedings of the IEEE.

[19]  Christoph Hardegen,et al.  A Study of Deep Learning for Network Traffic Data Forecasting , 2019, ICANN.

[20]  George Varghese,et al.  P4: programming protocol-independent packet processors , 2013, CCRV.

[21]  Jingyu Wang,et al.  Toward Greater Intelligence in Route Planning: A Graph-Aware Deep Learning Approach , 2020, IEEE Systems Journal.

[22]  Hong Liu,et al.  Jupiter Rising: A Decade of Clos Topologies and Centralized Control in Google's Datacenter Network , 2015, Comput. Commun. Rev..

[23]  Filip De Turck,et al.  Network Function Virtualization: State-of-the-Art and Research Challenges , 2015, IEEE Communications Surveys & Tutorials.

[24]  Song Guo,et al.  AI Routers & Network Mind: A Hybrid Machine Learning Paradigm for Packet Routing , 2019, IEEE Computational Intelligence Magazine.

[25]  Barbara Hammer,et al.  Incremental learning algorithms and applications , 2016, ESANN.

[26]  Albert Cabellos-Aparicio,et al.  Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN , 2019, SOSR.

[27]  Raouf Boutaba,et al.  Machine Learning for Cognitive Network Management , 2018, IEEE Communications Magazine.

[28]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[29]  Kaishun Wu,et al.  Living with Artificial Intelligence: A Paradigm Shift toward Future Network Traffic Control , 2018, IEEE Network.

[30]  Grenville J. Armitage,et al.  A survey of techniques for internet traffic classification using machine learning , 2008, IEEE Communications Surveys & Tutorials.

[31]  David A. Maltz,et al.  Network traffic characteristics of data centers in the wild , 2010, IMC '10.

[32]  Nei Kato,et al.  State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems , 2017, IEEE Communications Surveys & Tutorials.

[33]  Madeleine Glick,et al.  TCP flow classification and bandwidth aggregation in optically interconnected data center networks , 2016, IEEE/OSA Journal of Optical Communications and Networking.

[34]  Yuedong Xu,et al.  Demystifying Deep Learning in Networking , 2018, APNet '18.

[35]  Raouf Boutaba,et al.  A comprehensive survey on machine learning for networking: evolution, applications and research opportunities , 2018, Journal of Internet Services and Applications.

[36]  Dafna Shahaf,et al.  Learning to Route , 2017, HotNets.

[37]  Rubem Pereira,et al.  Future internet: trends and challenges , 2015, Int. J. Space Based Situated Comput..