# A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks

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## Abstract

**:**

## 1. Introduction

#### Machine Learning Tasks for Optical Networks

- Classification: Process of assigning threat categories;
- Regression: Predicting a value for items;
- Ranking: Ordering based on some criteria.

- Neural Networks;
- Support Vector Machines;
- Linear Regression;
- Principal Component Analysis;
- Statistical Models;
- Linear Time Series Models.

## 2. Motivations

#### 2.1. System Complexity

#### 2.2. Data Availability

## 3. Definitions

- specifically discusses short-term or long-term prediction purposes;
- is applied in simulation for the short term or long term;
- predicts traffic for short-term or long-term increments of time;
- is time-dependent or -independent.

## 4. Neural Networks

#### Relevant Papers

## 5. Support Vector Machines

#### Relevant Papers

## 6. Linear Regression

#### Relevant Papers

## 7. Principal Component Analysis

#### Relevant Papers

## 8. Statistical Models

#### 8.1. Hidden Markov Model

#### 8.2. Bayesian Estimation

## 9. Linear Time Series

#### Relevant Papers

## 10. Summary

## 11. Conclusions and Future Opportunities

## Author Contributions

## Funding

## Conflicts of Interest

## References

- Chen, L.P. Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar: Foundations of Machine Learning, second edition. Stat. Papers
**2019**, 60, 1793–1795. [Google Scholar] [CrossRef] - Jinno, M.; Takara, H.; Kozicki, B. Concept and Enabling Technologies of Spectrum-Sliced Elastic Optical Path Network (SLICE). In Proceedings of the Asia Communications and Photonics Conference and Exhibition, Shanghai, China, 2–6 November 2009. [Google Scholar] [CrossRef]
- Gerstel, O.; Jinno, M.; Lord, A.; Yoo, S.J.B. Elastic optical networking: A new dawn for the optical layer? IEEE Commun. Mag.
**2012**, 50, 12–20. [Google Scholar] [CrossRef] - Richardson, D.J.; Fini, J.M.; Nelson, L.E. Space-division multiplexing in optical fibres. Nat. Photonics
**2013**, 7, 354–362. [Google Scholar] [CrossRef][Green Version] - Tomkos, I. Toward the 6G Network Era: Opportunities and Challenges. IT Prof.
**2020**, 22, 34–38. [Google Scholar] [CrossRef] - Jain, R.; Subharthi, P. Network Virtualization and Software Defined Networking for Cloud Computing: A Survey. IEEE Commun. Mag.
**2013**, 51, 24–31. [Google Scholar] [CrossRef] - Mahmoud, Q.H. Cognitive Networks: Towards Self-Aware Networks; John Wiley & Sons, Ltd.: Hoboken, NJ, USA, 2007. [Google Scholar] [CrossRef]
- Cisco. Global Cloud Index: Forecast and Methodology, 2016–2021 (White Paper); Technical Report; CISCO: San Jose, CA, USA, 2018. [Google Scholar]
- Rak, J. Resilient Routing in Communication Networks; Computer Communications and Networks; Springer International Publishing: Berlin/Heidelberg, Germany, 2015; pp. 11–43. [Google Scholar] [CrossRef]
- Woo, W.L. Future trends in IM: Human-machine co-creation in the rise of AI. IEEE Instrum. Meas. Mag.
**2020**, 23, 71–73. [Google Scholar] [CrossRef] - Musumeci, F.; Rottondi, C.; Nag, A.; Macaluso, I.; Zibar, D.; Ruffini, M.; Tornatore, M. An Overview on Application of Machine Learning Techniques in Optical Networks. arXiv
**2018**, arXiv:1803.07976v1. [Google Scholar] [CrossRef][Green Version] - Rad, M.M.; Fouli, K.; Fathallah, H.A.; Rusch, L.A.; Maier, M. Passive optical network monitoring: Challenges and requirements. IEEE Commun. Mag.
**2011**, 49, S45–S52. [Google Scholar] [CrossRef] - Gu, R.; Yang, Z.; Ji, Y. Machine learning for intelligent optical networks: A comprehensive survey. J. Netw. Comput. Appl.
**2020**, 157, 102576. [Google Scholar] [CrossRef][Green Version] - Aibin, M. Traffic prediction based on machine learning for elastic optical networks. Opt. Switch. Netw.
**2018**, 30, 33–39. [Google Scholar] [CrossRef] - Aibin, M.; Walkowiak, K. Monte Carlo Tree Search with Last-Good-Reply Policy for Cognitive Optimization of Cloud-Ready Optical Networks. J. Netw. Syst. Manag.
**2020**, 28, 1722–1744. [Google Scholar] [CrossRef] - Bengio, Y. Learning Deep Architectures for AI. Found. Trends Mach. Learn.
**2009**, 2, 1–127. [Google Scholar] [CrossRef] - Zang, Y.; Ni, F.; Feng, Z.; Cui, S.; Ding, Z. Wavelet transform processing for cellular traffic prediction in machine learning networks. In Proceedings of the 2015 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2015—Proceedings, Chengdu, China, 12–15 July 2015; pp. 458–462. [Google Scholar] [CrossRef]
- Ren, G.; Cao, Y.; Wen, S.; Huang, T.; Zeng, Z. A modified Elman neural network with a new learning rate scheme. Neurocomputing
**2018**, 286, 11–18. [Google Scholar] [CrossRef] - Aibin, M. Deep Learning for Cloud Resources Allocation: Long-Short Term Memory in EONs. In Proceedings of the International Conference on Transparent Optical Networks, Angers, France, 9–13 July 2019; pp. 8–11. [Google Scholar]
- Aibin, M.; Chung, N.; Gordon, T.; Lyford, L.; Vinchoff, C. On Short-and Long-Term Traffic Prediction in Optical Networks Using Machine Learning. In Proceedings of the 25th International Conference on Optical Network Design and Modelling, ONDM 2021, Gothenburg, Sweden, 28 June–1 July 2021. [Google Scholar] [CrossRef]
- Chung, J.; Gulcehre, C.; Cho, K.; Bengio, Y. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. arXiv
**2014**, arXiv:1412.3555. [Google Scholar] - Peng, G. CDN: Content Distribution Network. arXiv
**2004**, arXiv:cs.NI/0411069. [Google Scholar] - Jaeger, H. Echo state network. Scholarpedia
**2007**, 2, 2330. [Google Scholar] [CrossRef] - Jia, W.B.; Xu, Z.Q.; Ding, Z.; Wang, K. An efficient routing and spectrum assignment algorithm using prediction for elastic optical networks. In Proceedings of the 2016 International Conference on Information System and Artificial Intelligence, ISAI 2016, Hong Kong, China, 24–26 June 2017; pp. 89–93. [Google Scholar] [CrossRef]
- Morales, F.; Ruiz, M.; Gifre, L.; Contreras, L.M.; López, V.; Velasco, L. Virtual Network Topology Adaptability Based on Data Analytics for Traffic Prediction. J. Opt. Commun. Netw.
**2017**, 9, A35–A45. [Google Scholar] [CrossRef] - Menezes, J.M.P.; Barreto, G.A. Long-term time series prediction with the NARX network: An empirical evaluation. Neurocomputing
**2008**, 71, 3335–3343. [Google Scholar] [CrossRef] - Sum, J.; Kan, W.K.; Young, G. A Note on the Equivalence of NARX and RNN. Neural Comput. Appl.
**2014**, 8, 33–39. [Google Scholar] [CrossRef] - Schaffer, C. Overfitting Avoidance as Bias. Mach. Learn.
**1993**, 10, 153–178. [Google Scholar] [CrossRef][Green Version] - Xiong, Y.; Shi, J.; Lv, Y.; Rouskas, G.N. Power-aware lightpath management for SDN-based elastic optical networks. In Proceedings of the 2017 26th International Conference on Computer Communications and Networks, ICCCN 2017, Vancouver, BC, Canada, 31 July–3 August 2017. [Google Scholar] [CrossRef]
- Bolla, R.; Bruschi, R.; Lago, P. The hidden cost of network low power idle. In Proceedings of the IEEE International Conference on Communications, Budapest, Hungary, 9–13 June 2013; pp. 4148–4153. [Google Scholar] [CrossRef]
- Kennedy, J.; Eberhart, R. Particle swarm optimization. In Proceedings of the ICNN’95—International Conference on Neural Networks, Perth, Australia, 27 November–1 December 1995; Volume 4, pp. 1942–1948. [Google Scholar] [CrossRef]
- Leung, H.C.; Leung, C.S.; Wong, E.W.; Li, S. Extreme learning machine for estimating blocking probability of bufferless OBS/OPS networks. J. Opt. Commun. Netw.
**2017**, 9, 682–692. [Google Scholar] [CrossRef] - Vinchoff, C.; Chung, N.; Gordon, T.; Lyford, L.; Aibin, M. Traffic Prediction in Optical Networks Using Graph Convolutional Generative Adversarial Networks. In Proceedings of the International Conference on Transparent Optical Networks, Bari, Italy, 19–23 July 2020; pp. 3–6. [Google Scholar]
- Noble, W.S. What is a support vector machine? Nat. Biotechnol.
**2006**, 24, 1565–1567. [Google Scholar] [CrossRef] - Mata, J.; De Miguel, I.; Durán, R.J.; Aguado, J.C.; Merayo, N.; Ruiz, L.; Fernández, P.; Lorenzo, R.M.; Abril, E.J. A SVM approach for lightpath QoT estimation in optical transport networks. In Proceedings of the 2017 IEEE International Conference on Big Data, Big Data 2017, Boston, MA, USA, 11–14 December 2017; pp. 4795–4797. [Google Scholar] [CrossRef][Green Version]
- Stepanov, N.; Alekseeva, D.; Ometov, A.; Lohan, E.S. Applying Machine Learning to LTE Traffic Prediction: Comparison of Bagging, Random Forest, and SVM. In Proceedings of the International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, Brno, Czech Republic, 5–7 October 2020; pp. 119–123. [Google Scholar] [CrossRef]
- Feng, H.; Shu, Y.; Wang, S.; Ma, M. SVM-based models for predicting WLAN traffic. In Proceedings of the IEEE International Conference on Communications, Istanbul, Turkey, 11–15 June 2006; Volume 2, pp. 597–602. [Google Scholar] [CrossRef][Green Version]
- Chen, W.; Shang, Z.; Chen, Y.; Chaeikar, S.S. A Novel Hybrid Network Traffic Prediction Approach Based on Support Vector Machines. J. Comput. Netw. Commun.
**2019**, 2019, 2182803. [Google Scholar] [CrossRef] - Mata, J.; de Miguel, I.; Durán, R.J.; Merayo, N.; Singh, S.K.; Jukan, A.; Chamania, M. Artificial intelligence (AI) methods in optical networks: A comprehensive survey. Opt. Switch. Netw.
**2018**, 28, 43–57. [Google Scholar] [CrossRef] - Lechowicz, P. Regression-based fragmentation metric and fragmentation-aware algorithm in spectrally-spatially flexible optical networks. Comput. Commun.
**2021**, 175, 156–176. [Google Scholar] [CrossRef] - Rai, S.; Garg, A.K. Analysis of RWA in WDM optical networks using machine learning for traffic prediction and pattern extraction. J. Opt.
**2021**, 1–8. [Google Scholar] [CrossRef] - Huang, Y.; Samoud, W.; Gutterman, C.L.; Ware, C.; Lourdiane, M.; Zussman, G.; Samadi, P.; Bergman, K. A Machine Learning Approach for Dynamic Optical Channel Add/Drop Strategies that Minimize EDFA Power Excursions|VDE Conference Publication|IEEE Xplore. In Proceedings of the European Conference on Optical Communication, Anaheim, CA, USA, 20–24 March 2016. [Google Scholar]
- Abadi, M.; Barham, P.; Chen, J.; Chen, Z.; Davis, A.; Dean, J.; Devin, M.; Ghemawat, S.; Irving, G.; Isard, M.; et al. TensorFlow: A System for Large-Scale Machine Learning This paper is included in the Proceedings of the TensorFlow: A system for large-scale machine learning. In Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation, Savannah, GA, USA, 2–4 November 2016. [Google Scholar] [CrossRef]
- Filho, R.H.; Maia, J.E.B. Network traffic prediction using PCA and K-means. In Proceedings of the 2010 IEEE/IFIP Network Operations and Management Symposium, NOMS 2010, Osaka, Japan, 19–23 April 2010; pp. 938–941. [Google Scholar] [CrossRef]
- De Araújo, D.R.; Bastos-Filho, C.J.; Martins-Filho, J.F. Methodology to obtain a fast and accurate estimator for blocking probability of optical networks. J. Opt. Commun. Netw.
**2015**, 7, 380–391. [Google Scholar] [CrossRef] - Xing, X.; Zhou, X.; Hong, H.; Huang, W.; Bian, K.; Xie, K. Traffic Flow Decomposition and Prediction Based on Robust Principal Component Analysis. In Proceedings of the IEEE Conference on Intelligent Transportation Systems, Proceedings ITSC, Gran Canaria, Spain, 15–18 September 2015; pp. 2219–2224. [Google Scholar] [CrossRef]
- Djukic, T.; Flötteröd, G.; Van Lint, H.; Hoogendoorn, S. Efficient real time OD matrix estimation based on Principal Component Analysis. In Proceedings of the IEEE Conference on Intelligent Transportation Systems, Proceedings ITSC, Anchorage, AK, USA, 16–19 September 2012; pp. 115–121. [Google Scholar] [CrossRef][Green Version]
- Fernandes, G.; Rodrigues, J.J.; Proença, M.L. Autonomous profile-based anomaly detection system using principal component analysis and flow analysis. Appl. Soft Comput.
**2015**, 34, 513–525. [Google Scholar] [CrossRef] - Jin, X.; Zhang, Y.; Yao, D. Simultaneously Prediction of Network Traffic Flow Based on PCA-SVR. In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Springer: Berlin/Heidelberg, Germany, 2007; Volume 4492, pp. 1022–1031. [Google Scholar] [CrossRef]
- Chitra, K.; Senkumar, M.R. Hidden Markov model based lightpath establishment technique for improving QoS in optical WDM networks. In Proceedings of the 2nd International Conference on Current Trends in Engineering and Technology, ICCTET 2014, Coimbatore, India, 8 July 2014; pp. 53–62. [Google Scholar] [CrossRef]
- Eddy, S.R. What is a hidden Markov model? Nat. Biotechnol.
**2004**, 22, 1315–1316. [Google Scholar] [CrossRef][Green Version] - Aibin, M.; Walkowiak, K.; Haeri, S.; Trajkovic, L. Traffic Prediction for Inter-Data Center Cross-Stratum Optimization Problems. In Proceedings of the IEEE Internation Conference on Computing, Networks and Communication, Maui, HI, USA, 5–8 March 2018. [Google Scholar]
- Dias, M.P.I.; Karunaratne, B.S.; Wong, E. Bayesian estimation and prediction-based dynamic bandwidth allocation algorithm for sleep/doze-mode passive optical networks. J. Light. Technol.
**2014**, 32, 2560–2568. [Google Scholar] [CrossRef] - Zhong, Z.; Hua, N.; Tornatore, M.; Li, J.; Li, Y.; Zheng, X.; Mukherjee, B. Provisioning Short-Term Traffic Fluctuations in Elastic Optical Networks. In Proceedings of the International Conference on Transparent Optical Networks, Angers, France, 9–13 July 2019. [Google Scholar]
- Shumway, R.; Stoo, D.S. Springer Texts in Statistics Time Series Analysis and Its Applications With R Examples, 4th ed.; Springer: Berlin/Heidelberg, Germany, 2017. [Google Scholar]
- Stellwagen, E.; Tashman, L. ARIMA: The Models of Box and Jenkins. Foresight Int. J. Appl. Forecast.
**2013**, 30, 28–33. [Google Scholar] - Hoong, N.K.; Hoong, P.K.; Tan, I.K.; Muthuvelu, N.; Seng, L.C. Impact of utilizing forecasted network traffic for data transfers|IEEE Conference Publication|IEEE Xplore. In Proceedings of the International Conference on Advanced Communication Technology (ICACT2011), Gangwon, Korea, 13–16 February 2011. [Google Scholar]
- Tan, I.K.; Hoong, P.K.; Keong, C.Y. Towards forecasting low network traffic for software patch downloads: An ARMA model forecast using CRONOS. In Proceedings of the 2nd International Conference on Computer and Network Technology, ICCNT 2010, Bangkok, Thailand, 23–25 April 2010; pp. 88–92. [Google Scholar] [CrossRef]
- Sadek, N.; Khotanzad, A. Multi-scale high-speed network traffic prediction using k-factor Gegenbauer ARMA model. In Proceedings of the IEEE International Conference on Communications, Paris, France, 20–24 June 2004; Volume 4, pp. 2148–2152. [Google Scholar] [CrossRef]
- Moayedi, H.Z.; Masnadi-Shirazi, M.A. Arima model for network traffic prediction and anomaly detection. In Proceedings of the International Symposium on Information Technology 2008, ITSim, Kuala Lumpur, Malaysia, 26–29 August 2008; Volume 3. [Google Scholar] [CrossRef]

Technique | Reference | Type | Local/Wide | Metric | Application |
---|---|---|---|---|---|

NN | 17 | NN | Wide | Traffic Volume | Cellular traffic |

19 | LSTM | Wide | Blocking Probability | Optical networks | |

20 | GCN-GAN | Wide | Traffic Volume | Elastic optical networks | |

21 | RNN | Wide | Traffic Volume | Communication networks | |

24 | BPNN | Wide | Blocking Probability | Elastic optical networks | |

25 | LSTM | Wide | Traffic Volume | Big data oriented networks | |

30 | NN | Wide | Traffic Volume | Universal | |

32 | ELM | Wide | Traffic Volume | Bufferless OBS/OPS networks | |

SVM | 36 | SVM | Wide | Traffic Volume | LTE networks |

37 | SVM | Local | Traffic Volume | Wireless Local Area Networks | |

38 | Hybrid SVM | Wide | Traffic Volume | Metro network | |

PCA | 44 | PCA | Wide | Traffic Volume | IP network backbone |

45 | PCA | Wide | Blocking Probability | Optical networks | |

47 | PCA | Local | Traffic Volume | Bluetooth networks | |

49 | PCA | Wide | Traffic Volume | Metro networks | |

Statistical Model | 52 | Markov Decision Process | Wide | Blocking Probability | Optical networks |

53 | Bayesian Estimation | Local | Traffic Volume | ONU | |

54 | Statistical analysis | Wide | Traffic Volume | Elastic optical networks | |

Linear Time Series | 57 | ARMA | Local | Traffic Volume | TCP traffic |

58 | GARMA | Local | Traffic Volume | MPEG, JPEG, Ethernet, Internet |

Technique | Reference | Type | Local/Wide | Metric | Application |
---|---|---|---|---|---|

NN | 14 | ANN | Wide | Blocking Probability | Optical networks |

19 | LSTM | Wide | Blocking Probability | Optical networks | |

20 | GCN-GAN | Wide | Traffic Volume | Traffic prediction | |

26 | NARX | Local | Traffic Volume | Traffic prediction | |

33 | GCN-GAN | Wide | Traffic Volume | Traffic prediction | |

SVM | 35 | SVM | Wide | QoT prediction | Optical transport networks |

Linear Regression | 40 | Linear Regression | Wide | Fragmentation prediction | Spectrally–Spatially Flexible Optical Networks |

Statistical Models | 50 | Hidden Markov Model | Wide | Traffic Volume | Wavelength Division Multiplexing networks |

Linear Time Series | 57 | ESN | Wide | Traffic Volume | Wireless traffic load |

60 | ARIMA | Wide | Traffic Volume | Communication networks |

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**MDPI and ACS Style**

Chen, A.; Law, J.; Aibin, M. A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks. *Telecom* **2021**, *2*, 518-535.
https://doi.org/10.3390/telecom2040029

**AMA Style**

Chen A, Law J, Aibin M. A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks. *Telecom*. 2021; 2(4):518-535.
https://doi.org/10.3390/telecom2040029

**Chicago/Turabian Style**

Chen, Aaron, Jeffrey Law, and Michal Aibin. 2021. "A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks" *Telecom* 2, no. 4: 518-535.
https://doi.org/10.3390/telecom2040029