Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32586
Traffic Forecasting for Open Radio Access Networks Virtualized Network Functions in 5G Networks

Authors: Khalid Ali, Manar Jammal


In order to meet the stringent latency and reliability requirements of the upcoming 5G networks, Open Radio Access Networks (O-RAN) have been proposed. The virtualization of O-RAN has allowed it to be treated as a Network Function Virtualization (NFV) architecture, while its components are considered Virtualized Network Functions (VNFs). Hence, intelligent Machine Learning (ML) based solutions can be utilized to apply different resource management and allocation techniques on O-RAN. However, intelligently allocating resources for O-RAN VNFs can prove challenging due to the dynamicity of traffic in mobile networks. Network providers need to dynamically scale the allocated resources in response to the incoming traffic. Elastically allocating resources can provide a higher level of flexibility in the network in addition to reducing the OPerational EXpenditure (OPEX) and increasing the resources utilization. Most of the existing elastic solutions are reactive in nature, despite the fact that proactive approaches are more agile since they scale instances ahead of time by predicting the incoming traffic. In this work, we propose and evaluate traffic forecasting models based on the ML algorithm. The algorithms aim at predicting future O-RAN traffic by using previous traffic data. Detailed analysis of the traffic data was carried out to validate the quality and applicability of the traffic dataset. Hence, two ML models were proposed and evaluated based on their prediction capabilities.

Keywords: O-RAN, traffic forecasting, NFV, ARIMA, LSTM, elasticity.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 379


[1] M. F. Bari, S. R. Chowdhury, R. Ahmed, R. Boutaba, and O. C. M. B. Duarte, “Orchestrating Virtualized Network Functions,” IEEE Trans. Netw. Serv. Manag., vol. 13, no. 4, pp. 725–739, 2016.
[2] Y. Gu, Y. Hu, Y. Ding, J. Lu, and J. Xie, “Elastic virtual network function orchestration policy based on workload prediction,” IEEE Access, vol. 7, pp. 96868–96878, 2019.
[3] R. Mijumbi, J. Serrat, J. L. Gorricho, N. Bouten, F. De Turck, and R. Boutaba, “Network function virtualization: State-of-the-art and research challenges,” IEEE Commun. Surv. Tutorials, vol. 18, no. 1, pp. 236–262, 2016.
[4] 5G PPP Architecture Working Group, “View on 5G Architecture,” Version 3.0, pp. 21–470, (Online). Available:, 2019.
[5] EMA, “Reducing Operational Expense (OpEx) with Virtualization and Virtual Systems Management,” (Online)., 2009.
[6] M. Ghaznavi, A. Khan, N. Shahriar, K. Alsubhi, R. Ahmed, and R. Boutaba, “Elastic virtual network function placement,” IEEE 4th Int. Conf. Cloud Networking, CloudNet, pp. 255–260, 2015
[7] A. Laghrissi and T. Taleb, “A Survey on the Placement of Virtual Resources and Virtual Network Functions,” IEEE Commun. Surv. Tutorials, vol. 21, no. 2, pp. 1409–1434, 2019
[8] O-RAN Alliance, “O-RAN Use Cases and Deployment Scenarios Towards Open and Smart RAN,” 2020.
[9] Q. Yuan, H. Tang, Y. Zhao, and X. Wang, “An approach for virtual network function deployment based on pooling in vEPC,” IEICE Trans. Commun., vol. E101B, no. 6, pp. 1398–1410, 2018
[10] R. Cohen, L. Lewin-Eytan, J. S. Naor, and D. Raz, “Near optimal placement of virtual network functions,” Proc. - IEEE INFOCOM, vol. 26, pp. 1346–1354, 2015
[11] C. H. T. Arteaga, F. Rissoi, and O. M. C. Rendon, “An adaptive scaling mechanism for managing performance variations in network functions virtualization: A case study in an NFV-based EPC,” 13th Int. Conf. Netw. Serv. Manag. CNSM, vol. 2018-Janua, pp. 1–7, 2017.
[12] X. Wang, C. Wu, F. Le, A. Liu, Z. Li, and F. Lau, “Online VNF scaling in datacenters,” IEEE Int. Conf. Cloud Comput. CLOUD, no. 1, pp. 140–147, 2017
[13] X. Fei, F. Liu, H. Xu, and H. Jin, “Adaptive VNF Scaling and Flow Routing with Proactive Demand Prediction,” Proc. - IEEE INFOCOM, pp. 486–494, 2018
[14] X. Zhang, C. Wu, Z. Li, and F. C. M. Lau, “Proactive VNF provisioning with multi-timescale cloud resources: Fusing online learning and online optimization,” Proc. - IEEE INFOCOM, 2017
[15] A. Bilal, T. Tarik, A. Vajda, and B. Miloud, “Dynamic cloud resource scheduling in virtualized 5G mobile systems,” IEEE Glob. Commun. Conf. GLOBECOM 2016 - Proc., pp. 0–5, 2016
[16] S. Clayman, E. Maini, A. Galis, A. Manzalini, and N. Mazzocca, “The dynamic placement of virtual network functions,” IEEE/IFIP NOMS 2014 - IEEE/IFIP Netw. Oper. Manag. Symp. Manag. a Softw. Defin. World, 2014
[17] AWS, “Amazon EC2 Secure and resizable compute capacity to support virtually any workload,”., 2021.
[18] E. Hormozi, H. Hormozi, M. K. Akbari, and M. S. Javan, “Using of machine learning into cloud environment (a survey): Managing and scheduling of resources in cloud systems,” Proc. - 7th Int. Conf. P2P, Parallel, Grid, Cloud Internet Comput. 3PGCIC, pp. 363–368, 2012.
[19] G. Brataas, E. Stav, S. Lehrig, S. Becker, G. Kopčak, and D. Huljenic, “CloudScale: Scalability management for cloud systems,” ICPE - Proc. ACM/SPEC Int. Conf. Perform. Eng., pp. 335–338, 2013.
[20] Z. Gong, X. Gu, and J. Wilkes, “PRESS: PRedictive Elastic reSource Scaling for cloud systems,” Proc. Int. Conf. Netw. Serv. Manag. CNSM 2010, pp. 9–16, 2010.
[21] M. Sedaghat, F. Hernandez-Rodriguez, and E. Elmroth, “A virtual machine re-packing approach to the horizontal vs. vertical elasticity trade-off for cloud autoscaling,” ACM Int. Conf. Proceeding Ser., 2013.
[22] ETSI, “Network functions virtualisation- an introduction, benefits, enablers, challenges & call for action,” SDN OpenFlow World Congr., no. 1, 2012.
[23] R. Mijumbi, S. Hasija, S. Davy, A. Davy, B. Jennings, and R. Boutaba, “Topology-Aware Prediction of Virtual Network Function Resource Requirements,” IEEE Trans. Netw. Serv. Manag., vol. 14, no. 1, pp. 106–120, 2017.
[24] F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini, “The graph neural network model,” IEEE Trans. Neural Networks, vol. 20, no. 1, pp. 61–80, 2009.
[25] I. Sarrigiannis, K. Ramantas, E. Kartsakli, P. V. Mekikis, A. Antonopoulos, and C. Verikoukis, “Online VNF Lifecycle Management in an MEC-Enabled 5G IoT Architecture,” IEEE Internet Things J., vol. 7, no. 5, pp. 4183–4194, 2020.
[26] D. M. Gutierrez-Estevez et al., “The path towards resource elasticity for 5G network architecture,” IEEE Wirel. Commun. Netw. Conf. Work. WCNCW, pp. 214–219, 2018.
[27] M. A. Sharkh, Y. Xu, and E. Leyder, “CloudMach: Cloud Computing Application Performance Improvement through Machine Learning,” Can. Conf. Electr. Comput. Eng., 2020.
[28] T. Taleb, K. Samdanis, B. Mada, H. Flinck, S. Dutta, and D. Sabella, “On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration,” IEEE Commun. Surv. Tutorials, vol. 19, no. 3, pp. 1657–1681, 2017.
[29] SAMSUNG, “Open RAN - The Open Road to 5G,” SAMSUNG White Paper, ungbusiness/pdfs/Open-RAN-The-Open-Road-to-5G.pdf, 2019.
[30] J. Keeney, M. Skorupski, G. Clapp, D. Kim, H. Eiselt, and R. Lovell, “O-RAN Software Community For inclusion from Release A Non Real-Time RAN Intelligent Controller (RIC non-RT).”
[31] R. Hyndman and A. Kostenko, “Minimum Sample Size Requirements for Seasonal Forecasting Models,” Foresight Int. J. Appl. Forecast., no. 6, pp. 12–15, 2007.
[32] G. Barlacchi et al., “A multi-source dataset of urban life in the city of Milan and the Province of Trentino,” Sci. Data, vol. 2, pp. 1–15, 2015.
[33] C. Gijón, M. Toril, S. Luna-Ramírez, M. L. Marí-Altozano, and J. M. Ruiz-Avilés, “Long-term data traffic forecasting for network dimensioning in lte with short time series,” Electron., vol. 10, no. 10, 2021.