A Survey: Network Feature Measurement Based on Machine Learning
Source abstract
Abstract
In network management, network measuring is crucial. Accurate network measurements can increase network utilization, network management, and the ability to find network problems promptly. With extensive technological advancements, the difficulty for network measurement is not just the growth in users and traffic but also the increasingly difficult technical problems brought on by the network’s design becoming more complicated. In recent years, network feature measurement issues have been extensively solved by the use of ML approaches, which are ideally suited to thorough data analysis and the investigation of complicated network behavior. However, there is yet no favored learning model that can best address the network measurement issue. The problems that ML applications in the field of network measurement must overcome are discussed in this study, along with an analysis of the current characteristics of ML algorithms in network measurement. Finally, network measurement techniques that have been used as ML techniques are examined, and potential advancements in the field are explored and examined.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Network Security and Intrusion Detection · Advanced Photonic Communication Systems · Advanced Computing and Algorithms
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250306, China
Chern Institute of Mathematics, Nankai University, Tianjin 300071, China
Funding
National Natural Science Foundation of China · Shandong Provincial Natural Science Foundation of China · Beijing Nova Program of Science and Technology
References (83)
Show source-provided references
1. A new algorithm for optimization of quality of service in peer to peer wireless mesh networks10.1007/s11276-019-01982-z ↗
2. Artificial intelligence applications in the telecommunications industry10.1111/j.1468-0394.2007.00433.x ↗
3. Feldmann, A., Gasser, O., Lichtblau, F., Pujol, E., Poese, I., Dietzel, C., Wagner, D., Wichtlhuber, M., Tapiador, J., and Vallina-Rodriguez, N. (2020, January 27–29). The lockdown effect: Implications of the COVID-19 pandemic on internet traffic. Proceedings of the ACM Internet Measurement Conference, Virtual Event.10.1145/3419394.3423658 ↗
4. Application of machine learning in wireless networks: Key techniques and open issues10.1109/COMST.2019.2924243 ↗
5. A survey of networking applications applying the software defined networking concept based on machine learning10.1109/ACCESS.2019.2928564 ↗
6. Toward concurrent video multicast orchestration for caching-assisted mobile networks10.1109/TVT.2021.3119429 ↗
7. Blockchain-empowered collaborative task offloading for cloud-edge-device computing10.1109/JSAC.2022.3213358 ↗
8. An ensemble machine learning approach for enhanced path loss predictions for 4G LTE wireless networks10.1002/dac.5101 ↗
9. Predicting delay in IoT using deep learning: A multiparametric approach10.1109/ACCESS.2019.2915958 ↗
10. CorrAUC: A malicious bot-IoT traffic detection method in IoT network using machine-learning techniques10.1109/JIOT.2020.3002255 ↗
11. Wireless network intelligence at the edge10.1109/JPROC.2019.2941458 ↗
12. A survey on explainable artificial intelligence (xai): Toward medical xai10.1109/TNNLS.2020.3027314 ↗
13. Abdellah, A.R., Mahmood, O.A., Kirichek, R., and Paramonov, A. (2021). Machine learning algorithm for delay prediction in IoT and pactile internet. Future Internet, 13.10.3390/fi13120304 ↗
14. Artificial intelligence enabled wireless networking for 5G and beyond: Recent advances and future challenges10.1109/MWC.001.1900292 ↗
15. Machine learning for networking: Workflow, advances and opportunities10.1109/MNET.2017.1700200 ↗
16. Jiang, J., Sekar, V., Stoica, I., and Zhang, H. (2017, January 4–8). Unleashing the potential of data-driven networking. Proceedings of the International Conference on Communication Systems and Networks, Bengaluru, India.10.1007/978-3-319-67235-9_9 ↗
17. Machine learning and deep learning10.1007/s12525-021-00475-2 ↗
18. Ray, S. (2019, January 14–16). A quick review of machine learning algorithms. Proceedings of the 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), Faridabad, India.10.1109/COMITCon.2019.8862451 ↗
19. Machine learning: Algorithms, real-world applications and research directions10.1007/s42979-021-00592-x ↗
20. Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage10.1186/s40537-019-0219-y ↗
21. Radial basis function neural network path loss prediction model for LTE networks in multitransmitter signal propagation environments10.1002/dac.4680 ↗
22. Study of packet loss prediction using machine learning
23. Botta, A., Mocerino, G.E., Cilio, S., and Ventre, G. (2021, January 14–23). A machine learning approach for dynamic selection of available bandwidth measurement tools. Proceedings of the International Conference on Communications, Montreal, QC, Canada.10.1109/ICC42927.2021.9500749 ↗
24. Throughput prediction using machine learning in LTE and 5g networks
25. Hu, W., Wang, Z., and Sun, L. (2015, January 8–12). Guyot: A hybrid learning and model based RTT predictive approach. Proceedings of the IEEE International Conference on Communications, London, UK.10.1109/ICC.2015.7249260 ↗
26. Labonne, M., Lopez, J., Poletti, C., and Munier, J.B. (2021, January 7–11). WIP: Short-term flow based bandwidth forecasting using machine learning. Proceedings of the International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), Pisa, Italy.10.1109/WoWMoM51794.2021.00048 ↗
27. Abdellah, A.R., Mahmood, O.A., and Koucheryavy, A. (2020, January 5–7). Delay prediction in IoT using machine learning approach. Proceedings of the International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), Brno, Czech Republic.10.1109/ICUMT51630.2020.9222245 ↗
28. Khangura, S.K., and Akın, S. (2019, January 27–29). Measurement based online available bandwidth estimation employing reinforcement learning. Proceedings of the International Teletraffic Congress (ITC 31), Budapest, Hungary.10.1109/ITC31.2019.00022 ↗
29. Chen, L., Chou, C., and Wang, B. (November, January 30). A machine learning-based approach for estimating available bandwidth. Proceedings of the IEEE Region 10 Conference, Taipei, Taiwan.
30. Hága, P., Laki, S., Tóth, F., Csabai, I., Stéger, J., and Vattay, G. (2007, January 21–23). Neural network based available bandwidth estimation in the etomic infrastructure. Proceedings of the International Conference on Testbeds and Research Infrastructure for the Development of Networks and Communities, Lake Buena Vista, FL, USA.10.1109/TRIDENTCOM.2007.4444688 ↗
31. Nunes, B.A.A., Veenstra, K., Ballenthin, W., Lukin, S., and Obraczka, K. (August, January 31). A machine learning approach to end-to-end rtt estimation and its application to TCP. Proceedings of the International Conference on Computer Communications and Networks (ICCCN), Lahaina, HI, USA.
32. Jaiswal, A., Babu, A.R., Zadeh, M.Z., Banerjee, D., and Makedon, F. (2020). A survey on contrastive self-supervised learning. Technologies, 9.10.3390/technologies9010002 ↗
33. A review on linear regression comprehensive in machine learning10.38094/jastt1457 ↗
34. Jahromi, H.Z., Hamed, Z., Hines, A., and Delanev, D.T. (2018, January 3–6). Towards application-aware networking: ML-based end-to-end application KPI/QoE metrics characterization in SDN. Proceedings of the Tenth International Conference on Ubiquitous and Future Networks (ICUFN), Prague, Czech Republic.10.1109/ICUFN.2018.8436625 ↗
35. Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review10.1109/JSTARS.2020.3026724 ↗
36. Classification and regression trees10.1002/widm.8 ↗
37. Scikit-learn: Machine learning in Python
38. A machine learning approach for packet loss prediction in science flows10.1016/j.future.2019.07.053 ↗
39. A review of processing methods and classification algorithm for EEG signal10.2478/cjece-2020-0004 ↗
40. Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: Comparative analysis of ANN and SVM models10.1016/j.petrol.2020.108182 ↗
41. A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting10.1007/s40745-021-00344-x ↗
42. A machine learning approach to TCP throughput prediction10.1109/TNET.2009.2037812 ↗
43. An efficient XGBoost–DNN-based classification model for network intrusion detection system10.1007/s00521-020-04708-x ↗
44. Maier, C., Dorfinger, P., Du, J.L., Gschweitl, S., and Lusak, J. (2019, January 19–21). Reducing consumed data volume in bandwidth measurements via a machine learning approach. Proceedings of the Network Traffic Measurement and Analysis Conference (TMA), Paris, France.10.23919/TMA.2019.8784575 ↗
45. Bhatt, D., Patel, C., Talsania, H., Patel, J., Vaghela, R., Pandya, S., Modi, K., and Ghayvat, H. (2021). CNN variants for computer vision: History, architecture, application, challenges and future scope. Electronics, 10.10.3390/electronics10202470 ↗
46. A survey of the recent architectures of deep convolutional neural networks10.1007/s10462-020-09825-6 ↗
47. Sato, N., Oshiba, T., Nogami, K., Sawabe, A., and Satoda, K. (2017, January 3–6). Experimental comparison of machine learning based available bandwidth estimation methods over operational LTE networks. Proceedings of the IEEE Symposium on Computers and Communications (ISCC), Heraklion, Greece.10.1109/ISCC.2017.8024553 ↗
48. Working memory connections for LSTM10.1016/j.neunet.2021.08.030 ↗
49. A review of recurrent neural networks: LSTM cells and network architectures10.1162/neco_a_01199 ↗
50. Classification based on decision tree algorithm for machine learning10.38094/jastt20165 ↗
51. Khatouni, A.S., Soro, F., and Giordano, D. (2019, January 8–12). A machine learning application for latency prediction in operational 4g networks. Proceedings of the IFIP/IEEE Symposium on Integrated Network and Service Management (IM), Arlington, VA, USA.
52. Zhang, Y., Wen, J., Yang, G., He, Z., and Wang, J. (2019). Path loss prediction based on machine learning: Principle, method, and data expansion. Appl. Sci., 9.10.3390/app9091908 ↗
53. Machine learning in eigensubspace for network path identification and flow forecast10.1049/cmu2.12230 ↗
54. A survey on semi-, self-and unsupervised learning for image classification10.1109/ACCESS.2021.3084358 ↗
55. Dike, H.U., Zhou, Y., Deveerasetty, K.K., and Wu, Q. (2018, January 25–27). Unsupervised learning based on artificial neural network: A review. Proceedings of the 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS), Shenzhen, China.10.1109/CBS.2018.8612259 ↗
56. MacQueen, J. (July, January 21). Some methods for classification and analysis of multivariate observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, CA, USA.
57. Applications of deep reinforcement learning in communications and networking: A survey10.1109/COMST.2019.2916583 ↗
58. A comprehensive survey on transfer learning10.1109/JPROC.2020.3004555 ↗
59. Ouali, Y., Hudelot, C., and Tami, M. (2020). An overview of deep semi-supervised learning. arXiv.
60. Abdelkefi, A., and Jiang, Y. (2011, January 2–5). A structural analysis of network delay. Proceedings of the Ninth Annual Communication Networks and Services Research Conference, Ottawa, ON, Canada.10.1109/CNSR.2011.15 ↗
61. Zhang, X., Wang, Y., Geng, G., and Yu, J. (2021). Delay-optimized multicast tree packing in software-defined networks. IEEE Trans. Serv. Comput., 261–275.10.1109/TSC.2021.3106264 ↗
62. A tutorial on ultrareliable and low-latency communications in 6G: Integrating domain knowledge into deep learning10.1109/JPROC.2021.3053601 ↗
63. Guo, J., Cao, P., Wu, J., Xu, B., and Yang, J. (November, January 31). Path delay variationpPrediction model with machine learning. Proceedings of the IEEE International Conference on Solid-State and Integrated Circuit Technology (ICSICT), Qingdao, China.
64. A machine learning framework for TCP round-trip time estimation10.1186/1687-1499-2014-47 ↗
65. Abdellah, A.R., Mahmood, O.A.K., Paramonov, A., and Koucheryavy, A. (2019, January 28–30). IoT traffic prediction using multi-step ahead prediction with neural network. Proceedings of the International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), Dublin, Ireland.10.1109/ICUMT48472.2019.8970675 ↗
66. Yajnik, M., Moon, S., Kurose, J., and Towsley, D. (1999, January 21–25). Measurement and modelling of the temporal dependence in packet loss. Proceedings of the IEEE INFOCOM’99. Conference on Computer Communications, Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No. 99CH36320), New York, NY, USA.10.1109/INFCOM.1999.749301 ↗
67. RINGLM: A link-level packet loss monitoring solution for software-defined networks10.1109/JSAC.2019.2927063 ↗
68. Lazaris, A., and Prasanna, V.K. (2019, January 21–25). Deep learning models for aggregated network traffic prediction. Proceedings of the International Conference on Network and Service Management (CNSM), Halifax, NS, Canada.10.23919/CNSM46954.2019.9012669 ↗
69. Chen, T., and Guestrin, C. (2016, January 13–17). Xgboost: A scalable tree boosting system. Proceedings of the ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.10.1145/2939672.2939785 ↗
70. Elastic and reliable bandwidth reservation based on distributed traffic monitoring and control10.1109/TPDS.2022.3196840 ↗
71. Online available bandwidth estimation using multiclass supervised learning techniques10.1016/j.comcom.2021.02.009 ↗
72. Khangura, S.K., Fidler, M., and Rosenhahn, B. (2018, January 14–16). Neural networks for measurement based bandwidth estimation. Proceedings of the IFIP Networking Conference (IFIP Networking) and Workshops, Zurich, Switzerland.10.23919/IFIPNetworking.2018.8697023 ↗
73. Labonne, M., Chatzinakis, C., and Olivereau, A. (2020, January 15–18). Predicting bandwidth utilization on network links using machine learning. Proceedings of the European Conference on Networks and Communications (EuCNC), Dubrovnik, Croatia.10.1109/EuCNC48522.2020.9200910 ↗
74. Macrocell path-loss prediction using artificial neural networks10.1109/TVT.2010.2050502 ↗
75. Optimal model for path loss predictions using feed-forward neural networks10.1080/23311916.2018.1444345 ↗
76. Path loss modeling: A machine learning based approach using support vector regression and radial basis function models10.4236/ojapps.2022.126068 ↗
77. Singhal, P., and Yadav, A. (2014, January 6–8). Congestion detection in wireless sensor network using neural network. Proceedings of the International Conference for Convergence for Technology-2014, Pune, India.10.1109/I2CT.2014.7092259 ↗
78. Madalgi, J.B., and Kumar, S.A. (2018, January 20–22). Development of wireless sensor network congestion detection classifier using support vector machine. Proceedings of the International Conference on Computational Systems and Information Technology for Sustainable Solutions (CSITSS), Bengaluru, India.10.1109/CSITSS.2018.8768738 ↗
79. Beverly, R., Sollins, K., and Berger, A. (2006, January 15). SVM learning of IP address structure for latency prediction. Proceedings of the SIGCOMM Workshop on Mining Network Data, Pisa, Italy.10.1145/1162678.1162682 ↗
80. Network delay measurement with machine learning: From lab to real-world deployment10.1109/MIM.2022.9847199 ↗
81. Machine learning for measurement based bandwidth estimation10.1016/j.comcom.2019.05.005 ↗
82. Madalgi, J.B., and Kumar, S.A. (2017, January 21–23). Congestion detection in wireless sensor networks using MLP and classification by regression. Proceedings of the International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), Tumkur, India.10.1109/ICATCCT.2017.8389138 ↗
83. Cybersecurity data science: An overview from machine learning perspective10.1186/s40537-020-00318-5 ↗