ABSTRACT
We analyze 4G and 5G transport-layer sessions generated by a wide range of mobile services at over 282,000 base stations (BSs) of an operational mobile network, and carry out a statistical characterization of their demand rates, associated traffic volume and temporal duration. Based on the gained insights, we model the arrival process of sessions at heterogeneously loaded BSs, the distribution of the session-level load and its relationship with the session duration, using simple yet effective mathematical approaches. Our models are fine-tuned to a variety of services, and complement existing tools that mimic packet-level statistics or aggregated spatiotemporal traffic demands at mobile network BSs. They thus offer an original angle to mobile traffic data generation, and support a more credible performance evaluation of solutions for network planning and management. We assess the utility of the models in practical application use cases, demonstrating how they enable a more trustworthy evaluation of solutions for the orchestration of sliced and virtualized networks.
- 3GPP Technical Specification Group Services and System Aspects. 2020. TR:28.812 - Study on scenarios for Intent driven management services for mobile networks, Telecommunication management.Google Scholar
- 3GPP TR 36.814 V9.2.0. 2017. 3rd Generation Partnership Project; technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E-UTRA); Further advancements for E-UTRA physical layer aspects (Release 9). (Mar. 2017).Google Scholar
- 3GPP TR 36.888 V12.0.0. 2013. 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Study on provision of low-cost Machine-Type Communications (MTC) User Equipments (UEs) based on LTE (Release 12). (June 2013).Google Scholar
- 3GPP TS 23.288 v16.1.0. 2019. Architecture Enhancements for 5G System (5GS) to Support Network Data Analytics Services (Release 16). (June 2019).Google Scholar
- 3GPP TS 28.533 v16.0.0. 2019. Management and Orchestration of Networks and Network Slicing; Management and Orchestration Architecture (Release. (June 2019).Google Scholar
- 3GPP TSG-RAN1#48 R1-070674. 2007. LTE physical layer framework for performance verification. (Feb. 2007).Google Scholar
- Jose A. Ayala-Romero, Andres Garcia-Saavedra, Marco Gramaglia, Xavier Costa-Perez, Albert Banchs, and Juan J. Alcaraz. 2019. Vrain: a deep learning approach tailoring computing and radio resources in virtualized RANs. In ACM MobiCom '19. isbn: 9781450361699. https://doi.org/10.1145/3300061.3345431.Google ScholarDigital Library
- G. Barlacchi et al. 2015. A multi-source dataset of urban life in the city of Milan and the province of Trentino. Scientific Data, 2.Google Scholar
- Dario Bega, Marco Gramaglia, Marco Fiore, Albert Banchs, and Xavier Costa-Perez. 2020. Aztec: anticipatory capacity allocation for zero-touch network slicing. In IEEE INFOCOM '20, 794--803.Google ScholarDigital Library
- Biljana Bojovic and Sandra Lagen. 2022. Enabling NGMN mixed traffic models for Ns-3. In Proc. Workshop on Ns-3. ACM WNS3 '22, Virtual Event, USA, 127--134. isbn: 9781450396516. doi: 10.1145/3532577.3532602.Google ScholarDigital Library
- Deezer Support. 2022. Deezer audio quality. https://support.deezer.com/hc/en-gb/articles/115003865685-Deezer-Audio-Quality. Accessed: 2022-05-31. (2022).Google Scholar
- ETSI. 2019. GS ZSM 001 V1.1.1 - Zero-touch network and Service Management (ZSM); Requirements based on documented scenarios.Google Scholar
- European Union. 2016. Eu general data protection regulation (gdpr): regulation (eu) 2016/679 of the european parliament and of the council of 27 april 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95/46/ec (general data protection regulation). Retrieved October 18, 2021 from https://g dpr-info.eu/.Google Scholar
- Marco Helbich, Jamal Jokar Arsanjani, and Michael Leitner, editors. 2015. Towards a comparative science of cities: using mobile traffic records in new york, london, and hong kong. Computational Approaches for Urban Environments. Springer International Publishing, Cham, 363--387. isbn: 978-3-319-11469-9. doi: 10.1007/978-3-319-11469-9_15.Google ScholarCross Ref
- Himank Gupta, Mehul Sharma, Antony Franklin A., and Bheemarjuna Reddy Tamma. 2020. Apt-ran: a flexible split-based 5g ran to minimize energy consumption and handovers. IEEE Transactions on Network and Service Management, 17, 1.Google ScholarDigital Library
- Jin Huang and Ming Xiao. 2020. Mobile network traffic prediction based on seasonal adjacent windows sampling and conditional probability estimation. IEEE Transactions on Big Data, 1--1. doi: 10.1109/TBDATA.2020.3014049.Google ScholarCross Ref
- IEEE 802.16m-08/004r2. 2008. IEEE 802.16m evaluation methodology document (EMD). (July 2008).Google Scholar
- David Johnson. 1973. Near-optimal bin packing algorithms. PhD thesis.Google Scholar
- A. Karasaridis and D. Hatzinakos. 2001. Network heavy traffic modeling using /spl alpha/-stable self-similar processes. IEEE Transactions on Communications, 49, 7.Google ScholarCross Ref
- Hatem Khedher, Sahar Hoteit, Patrick Brown, Véronique Véque, Ruby Krishnaswamy, William Diego, and Makhlouf Hadji. 2020. Real traffic-aware scheduling of computing resources in cloud-ran. In ICNC '20, 422--427. doi: 10.1109/ICNC47757.2020.9049679.Google ScholarCross Ref
- Daegyeom Kim, Myeongjin Ko, Sunghyun Kim, Sungwoo Moon, Kyung-Yul Cheon, Seungkeun Park, Yunbae Kim, Hyungoo Yoon, and Yong-Hoon Choi. 2022. Design and implementation of traffic generation model and spectrum requirement calculator for private 5g network. IEEE Access, 10, 15978--15993. doi: 10.1109/ACCESS.2022.3149050.Google ScholarCross Ref
- Jinsung Lee et al. 2020. Perceive: deep learning-based cellular uplink prediction using real-time scheduling patterns. In ACM MobiSys '20, 377--390. isbn: 9781450379540.Google Scholar
- Rongpeng Li, Zhifeng Zhao, Chen Qi, Xuan Zhou, Yifan Zhou, and Hong-gang Zhang. 2015. Understanding the traffic nature of mobile instantaneous messaging in cellular networks: a revisiting to α-stable models. IEEE Access, 3.Google Scholar
- Rongpeng Li, Zhifeng Zhao, Jianchao Zheng, Chengli Mei, Yueming Cai, and Honggang Zhang. 2017. The learning and prediction of application-level traffic data in cellular networks. IEEE Transactions on Wireless Communications, 16, 6.Google ScholarDigital Library
- Yu-Ting Lin, Thomas Bonald, and Salah Eddine Elayoubi. 2018. Flow-level traffic model for adaptive streaming services in mobile networks. Computer Networks, 137, 1--16. doi: https://doi.org/10.1016/j.comnet.2018.01.027.Google ScholarDigital Library
- Zinan Lin, Alankar Jain, Chen Wang, Giulia Fanti, and Vyas Sekar. 2020. Using gans for sharing networked time series data: challenges, initial promise, and open questions. In ACM IMC '20. Virtual Event, USA, 464--483. isbn: 9781450381383. doi: 10.1145/3419394.3423643.Google ScholarDigital Library
- Cristina Marquez, Marco Gramaglia, Marco Fiore, Albert Banchs, Cezary Ziemlicki, and Zbigniew Smoreda. 2017. Not all apps are created equal: analysis of spatiotemporal heterogeneity in nationwide mobile service usage. In ACM CoNEXT '17. Incheon, Republic of Korea, 180--186. isbn: 9781450354226. doi: 10.1145/3143361.3143369.Google ScholarDigital Library
- Florian Metzger, Albert Rafetseder, Peter Romirer-Maierhofer, and Kurt Tutschku. 2014. Exploratory analysis of a ggsn's pdp context signaling load. Journal of Computer Networks and Communications, 526231. doi: https://doi.org/10.1155 /2014/526231.Google Scholar
- Eduardo Mucelli Rezende Oliveira, Aline Carneiro Viana, K.P. Naveen, and Carlos Sarraute. 2017. Mobile data traffic modeling: revealing temporal facets. Computer Networks, 112, 176--193. doi: https://doi.org/10.1016/j.comnet.2016.1 0.016.Google ScholarDigital Library
- Daniel Müllner. 2011. Modern hierarchical, agglomerative clustering algorithms. arXiv preprint arXiv:1109.2378.Google Scholar
- Jorge Navarro-Ortiz, Pablo Romero-Diaz, Sandra Sendra, Pablo Ameigeiras, Juan J. Ramos-Munoz, and Juan M. Lopez-Soler. 2020. A survey on 5g usage scenarios and traffic models. IEEE Communications Surveys & Tutorials, 22, 2, 905--929.Google ScholarCross Ref
- O-RAN.WG2.Non-RT-RIC-ARCH-TS-v01.00. 2021. O-RAN Non-RT RIC Architecture 1.0. (Oct. 2021).Google Scholar
- O-RAN.WG3.RICARCH-v02.01. 2022. O-RAN Near-RT RAN Intelligent Controller Near-RT RIC Architecture 2.01. (Mar. 2022).Google Scholar
- Michele Polese, Francesco Restuccia, and Tommaso Melodia. 2021. Deepbeam: deep waveform learning for coordination-free beam management in mmwave networks. In MobiHoc '21. ACM MobiHoc '21, Shanghai, China, 61--70.Google ScholarDigital Library
- Aaditya Ramdas, Nicolás García Trillos, and Marco Cuturi. 2017. On wasserstein two-sample testing and related families of nonparametric tests. Entropy, 19, 2. doi: 10.3390/e19020047.Google ScholarCross Ref
- Soha Rawas. 2021. Energy, network, and application-aware virtual machine placement model in SDN-enabled large scale cloud data centers. Multimedia Tools and App., 80, 10.Google ScholarDigital Library
- Peter J. Rousseeuw. 1987. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53--65. doi: https://doi.org/10.1016/0377-0427(87)90125-7.Google ScholarDigital Library
- Abraham. Savitzky and M. J. E. Golay. 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36, 8, 1627--1639. eprint: https://doi.org/10.1021/ac60214a047. doi: 10.1021/ac60214a047.Google ScholarCross Ref
- M. Zubair Shafiq, Lusheng Ji, Alex X. Liu, and Jia Wang. 2011. Characterizing and modeling internet traffic dynamics of cellular devices. In ACM SIGMETRICS '11. San Jose, California, USA, 305--316. isbn: 9781450308144. doi: 10.1145/1993 744.1993776.Google ScholarDigital Library
- Rajkarn Singh, Cengis Hasan, Xenofon Foukas, Marco Fiore, Mahesh K. Marina, and Yue Wang. 2021. Energy-efficient orchestration of metro-scale 5G radio access networks. In IEEE INFOCOM '21, 1--10. doi: 10.1109/INFOCOM42981.20 21.9488786.Google ScholarDigital Library
- Chuanhao Sun, Kai Xu, Marco Fiore, Mahesh K. Marina, Yue Wang, and Cezary Ziemlicki. 2022. Appshot: a conditional deep generative model for synthesizing service-level mobile traffic snapshots at city scale. IEEE Transactions on Network and Service Management, 19, 4, 4136--4150. doi: 10.1109/TNSM.2022.3199458.Google ScholarCross Ref
- Ilias Tsompanidis, Ahmed H. Zahran, and Cormac J. Sreenan. 2014. Mobile network traffic: a user behaviour model. In 2014 7th IFIP Wireless and Mobile Networking Conference (WMNC), 1--8. doi: 10.1109/WMNC.2014.6878862.Google ScholarCross Ref
- X. Wang, Z. Zhou, F. Xiao, K. Xing, Z. Yang, Y. Liu, and C. Peng. 2019. Spatio-temporal analysis and prediction of cellular traffic in metropolis. IEEE Trans. Mobile Comput., 18, 09, (Sept. 2019), 2190--2202.Google ScholarCross Ref
- Jing Wu, Ming Zeng, Xinlei Chen, Yong Li, and Depeng Jin. 2018. Characterizing and predicting individual traffic usage of mobile application in cellular network. In ACM UbiComp '18. Association for Computing Machinery, Singapore, Singapore, 852--861. isbn: 9781450359665. doi: 10.1145/3267305.3274173.Google ScholarDigital Library
- Shangbin Wu, Yue Wang, and Lu Bai. 2020. Deep convolutional neural network assisted reinforcement learning based mobile network power saving. IEEE Access, 8, 93671-93681. doi: 10.1109/ACCESS.2020.2995057.Google ScholarCross Ref
- K. Xu, R. Singh, H. Bilen, M. Fiore, M. K. Marina, and Y. Wang. 2022. Cartagenie: context-driven synthesis of city-scale mobile network traffic snapshots. In IEEE PerCom '22. Los Alamitos, CA, USA, (Mar. 2022), 119--129. doi: 10.1109/PerCo m53586.2022.9762395.Google ScholarCross Ref
- Kai Xu, Rajkarn Singh, Marco Fiore, Mahesh K. Marina, Hakan Bilen, Muhammad Usama, Howard Benn, and Cezary Ziemlicki. 2021. Spectragan: spectrum based generation of city scale spatiotemporal mobile network traffic data. In ACM CoNEXT '21. Virtual Event, Germany, 243--258. isbn: 9781450390989. doi: 10.1145/3485983.3494844.Google ScholarDigital Library
- Qiang Xu, Alexandre Gerber, Zhuoqing Morley Mao, and Jeffrey Pang. 2011. Acculoc: practical localization of performance measurements in 3G networks. In ACM MobiSys '11. Bethesda, Maryland, USA, 183--196. isbn: 9781450306430. doi: 10.1145/1999995.2000013.Google ScholarDigital Library
- 2020. Microscope: mobile service traffic decomposition for network slicing as a service. ACM MobiCom '20, 14 pages. isbn: 9781450370851.Google Scholar
Index Terms
- Characterizing and Modeling Session-Level Mobile Traffic Demands from Large-Scale Measurements
Recommendations
Modeling and regulation of host traffic in ATM networks
LCN '96: Proceedings of the 21st Annual IEEE Conference on Local Computer NetworksFor any connection admission control (CAC) algorithm to work correctly and efficiently, accurate information of the traffic flow out of the host systems is required. We develop several approximation approaches for modeling the traffic flows of hard real-...
New MPLS network management techniques based on adaptive learning
The combined use of the differentiated services (DiffServ) and multiprotocol label switching (MPLS) technologies is envisioned to provide guaranteed quality of service (QoS) for multimedia traffic in IP networks, while effectively using network ...
Link Sizing for Multi-Media Traffic Transported over IP
The objective of this study is to develop a model for multiplexed traffic carried over IP. The traffic carried by IP networks is expected to be a mixture of low speed voice and data traffic, along with high-speed video, images, and interactive traffic. ...
Comments