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Efficient 5G Mobile Network Management: Network Slicing and Global Roaming Optimization

dc.contributor.advisorFiore, Marco 
dc.contributor.advisorBanchs, Albert 
dc.contributor.authorAlcalá-Marín, Sergi 
dc.date.accessioned2025-03-06T11:34:31Z
dc.date.available2025-03-06T11:34:31Z
dc.date.issued2025-02-24
dc.identifier.citation[1] S. Alcalá-Marín, A. Raman, W. Wu, A. Lutu, M. Bagnulo, O. Alay, and F. Bustamante, “Global mobile network aggregators: taxonomy, roaming performance and optimization,” in Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services, 2022, pp. 183–195. [2] S. Alcalá-Marín, A. Bazco-Nogueras, A. Banchs, and M. Fiore, “kansaas: Combining deep learning and optimization for practical overbooking of network slices,” in Proceedings of the Twenty-Fourth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, ser. MobiHoc ’23. New York, NY, USA: Association for Computing Machinery, 2023, p. 51–60. [3] S. A. A-Marín, W. Wu, A. Raman, M. Bagnulo, O. Alay, E. Bustamante, M. Fiore, and A. Lutu, “A comparative analysis of global mobile network aggregators,” 2024. [4] S. Alcalá-Marín, D. Bega, M. Gramaglia, A. Banchs, X. Costa-Perez, and M. Fiore, “Aztec+: Long and short term resource provisioning for zero-touch network management,” 2024. [5] S. Davis, R. Batra, G. Blennerud, F. Burstedt, A. Carlsson, S. Elmgren, J. Jelic, D. Kobescak, I. Komljenovic, F. Kronestedt, C. Kuhlins, P. Lindberg, Y. Liu, N. Lövehagen, G. Macharia, J. Malmodin, J. Manssour, S. Onay, R. S. Pandey, T. Tolic, J. Travers, and B. Trollsås, “Ericsson mobility report letter from the publisher 5g standalone brings new opportunities,” 2023. [6] Q. Liu, T. Zhang, M. Hemmatpour, H. Qiu, D. Zhang, C. S. Chen, M. Mellia, and A. Aghasaryan, “Operationalizing ai/ml in future networks: A bird’s eye view from the system perspective,” IEEE Communications Magazine, pp. 1–7, 2024. [7] D. Bega, M. Gramaglia, M. Fiore, A. Banchs, and X. Costa-Perez, “Aztec: Anticipatory capacity allocation for zero-touch network slicing,” in IEEE INFOCOM 2020-IEEE Conference on Computer Communications. IEEE, 2020, pp. 794–803. [8] X. Foukas, G. Patounas, A. Elmokashfi, and M. K. Marina, “Network slicing in 5g: Survey and challenges,” Comm. Mag., vol. 55, no. 5, p. 94–100, May 2017. [Online]. Available: https://doi.org/10.1109/MCOM.2017.1600951 [9] H. P. Phyu, D. Naboulsi, and R. Stanica, “Machine learning in network slicing - a survey,” IEEE Access, vol. 11, pp. 39 123–39 153, 2023. [10] X. Li, M. Samaka, H. A. Chan, D. Bhamare, L. Gupta, C. Guo, and R. Jain, “Network slicing for 5g: Challenges and opportunities,” IEEE Internet Computing, vol. 21, no. 5, pp. 20–27, 2017. [11] I. Afolabi, T. Taleb, K. Samdanis, A. Ksentini, and H. Flinck, “Network slicing and softwarization: A survey on principles, enabling technologies, and solutions,” IEEE Communications Surveys & Tutorials, vol. 20, no. 3, pp. 2429–2453, 2018. [12] M. A. Alsheikh, S. Lin, D. Niyato, and H.-P. Tan, “Machine learning in wireless sensor networks: Algorithms, strategies, and applications,” IEEE Communications Surveys & Tutorials, vol. 16, no. 4, pp. 1996–2018, 2014. [13] 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,” Commun. Surveys Tuts., vol. 18, no. 1, p. 236–262, Jan. 2016. [Online]. Available: https://doi.org/10.1109/COMST.2015.2477041 [14] C. Marquez, M. Gramaglia, M. Fiore, A. Banchs, and X. Costa-Pérez, “How Should I Slice My Network?: A Multi-Service Empirical Evaluation of Resource Sharing Efficiency,” in Proc. of the 24th Annual International Conference on Mobile Computing and Networking (ACM MobiCom), New Delhi, India, Oct. 2018, pp. 191–206. [15] V. Sciancalepore, C. Mannweiler, F. Z. Yousaf, P. Serrano, M. Gramaglia, J. Bradford, and I. L. Pavón, “A Future-Proof Architecture for Management and Orchestration of Multi- Domain NextGen Networks,” IEEE Access, vol. 7, pp. 79 216–79 232, Jun. 2019. [16] A. Lutu, D. Perino, M. Bagnulo, E. Frias-Martinez, and J. Khangosstar, “A Characterization of the COVID-19 Pandemic Impact on a Mobile Network Operator Traffic,” in A Characterization of the COVID-19 Pandemic Impact on a Mobile Network Operator Traffic, ser. IMC’20. New York, NY, USA: Association for Computing Machinery, 2020, pp. 19 – 33. [Online]. Available: https://doi.org/10.1145/3419394.3423655 [17] A. Lutu, B. Jun, A. Finamore, F. E. Bustamante, and D. Perino, “Where Things Roam: Uncovering Cellular IoT/M2M Connectivity,” in Proceedings of the ACM Internet Measurement Conference, ser. IMC’20. New York, NY, USA: Association for Computing Machinery, 2020, pp. 147 – 161. [18] E. Obiodu, A. Raman, A. Abubakar, S. Mangiante, N. Sastry, and H. Aghvami, “Dsm-moc as baseline: Reliability assurance via redundant cellular connectivity in connected cars,” IEEE Transactions on Network and Service Management, pp. 1–1, 2022. [19] H. Zhang, N. Liu, X. Chu, K. Long, A.-H. Aghvami, and V. C. M. Leung, “Network slicing based 5g and future mobile networks: Mobility, resource management, and challenges,” IEEE Communications Magazine, vol. 55, no. 8, pp. 138–145, 2017. [20] C. Zhang, P. Patras, and H. Haddadi, “Deep learning in mobile and wireless networking: A survey,” IEEE Communications Surveys Tutorials, vol. 21, no. 3, pp. 2224–2287, Mar. 2019. [21] B. Koley, “The zero touch network,” in Proc. of the 12th International Conference on Network and Service Management (IEEE CNSM), Montreal, Quebec, Canada, Oct. 2016. [22] European Telecommunications Standards Institute (ETSI), “ZSM Scenarios and key requirements,” ETSI ISG ZSM 001, Oct. 2018. [23] 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,” Commun. Surveys Tuts., vol. 19, no. 3, p. 1657–1681, Jul. 2017. [Online]. Available: https://doi.org/10.1109/COMST.2017.2705720 [24] J. B. et al., “The big book of mlops,” https://www.databricks.com/p/ebook/ the-big-book-of-mlops, 2023, available at: https://www.databricks.com/p/ebook/the-bigbook- of-mlops. [25] Nokia Networks, “Ava ai and analytics,” https://nokia.com/networks/ai-and-analytics, 2022, available at: https://nokia.com/networks/ai-and-analytics. [26] Z. Y. et al., “Aquarius—enable fast, scalable, data-driven service management in the cloud,” IEEE Transactions on Network and Service Management, vol. 19, no. 4, pp. 4028–4044, 2022. [27] L. Y. et al., “Quality monitoring and assessment of deployed deep learning models for network aiops,” IEEE Network, vol. 35, no. 6, pp. 84–90, 2021. [28] J. H. et al., “New directions in automated traffic analysis,” in Proceedings of the ACM Conference on Computer and Communications Security (CCS), 2021, pp. 3366–3383. [29] C. Huyen, Designing machine learning systems: An Iterative Process for Production-ready Applications. O’Reilly Media, 2022. [30] C. Marquez, M. Gramaglia, M. Fiore, A. Banchs, and X. Costa-Pérez, “Resource sharing efficiency in network slicing,” IEEE Transactions on Network and Service Management, vol. 16, no. 3, pp. 909–923, 2019. [31] N. Bui and J. Widmer, “Data-driven evaluation of anticipatory networking in lte networks,” IEEE Transactions on Mobile Computing, vol. 17, no. 10, pp. 2252–2265, October 2018. [32] V. Sciancalepore, K. Samdanis, X. Costa-Perez, M. G. D. Bega, and A. Banchs, “Mobile traffic forecasting for maximizing 5g network slicing resource utilization,” in Proceedings of the IEEE Conference on Computer Communications (INFOCOM), May 2017, pp. 1–4. [33] J. Mei, X. Wang, , and K. Zheng, “An intelligent self-sustained ran slicing framework for diverse service provisioning in 5g-beyond and 6g networks,” Intelligent Converged Networks, vol. 1, pp. 281–294, December 2020. [34] F. Tonini, M. F. C. Natalino, C. Raffaelli, and P. Monti, “Network slicing automation: Challenges and benefits,” in Proceedings of the International Conference on Optical Network Design and Modeling (ONDM), May 2020, pp. 1–6. [35] T. Jirsik, S. Trcka, and P. Celeda, “Quality of service forecasting with lstm neural network,” in Proceedings of the IFIP/IEEE Symposium on Integrated Network and Service Management (IM), April 2019, pp. 251–260. [36] S. Siami-Namini, N. Tavakoli, and A. S. Namin, “A comparison of arima and lstm in forecasting time series,” in Proceedings of the 17th IEEE International Conference on Machine Learning and Applications (ICMLA), December 2018, pp. 1394–1401. [37] C.-W. Huang, C.-T. Chiang, , and Q. Li, “A study of deep learning networks on mobile traffic forecasting,” in Proceedings of the IEEE 28th Annual International Symposium on Personal, Indoor, Mobile Radio Communications (PIMRC), October 2017, pp. 1–6. [38] D. Bega, M. Gramaglia, M. Fiore, A. Banchs, and X. Costa-Perez, “DeepCog: Cognitive Network Management in Sliced 5G Networks with Deep Learning,” in IEEE INFOCOM 2019 - IEEE Conference on Computer Communications, 2019, pp. 280–288. [39] D. Bega, M. Gramaglia, A. Garcia-Saavedra, M. Fiore, A. Banchs, and X. Costa-Perez, “Network slicing meets artificial intelligence: An ai-based framework for slice management,” IEEE Communications Magazine, vol. 58, no. 6, pp. 32–38, June 2020. [40] T. V. K. Buyakar, H. Agarwal, B. R. Tamma, and A. A. Franklin, “Resource allocation with admission control for gbr and delay qos in 5g network slices,” in Proceedings of the International Conference on Communication Systems and Networks (COMSNETS), 2020, pp. 213–220. [41] S. K. Tayyaba, H. A. Khattak, A. Almogren, M. A. Shah, I. U. Din, I. Alkhalifa, , and M. Guizani, “5g vehicular network resource management for improving radio access through machine learning,” IEEE Access, vol. 8, pp. 6792–6800, 2020. [42] E. H. Bouzidi, A. Outtagarts, A. Hebbar, R. Langar, and R. Boutaba, “Online based learning for predictive end-to-end network slicing in 5g networks,” in Proceedings of the IEEE International Conference on Communications (ICC), June 2020, pp. 1–7. [43] J.-B. Monteil, J. Hribar, P. Barnard, Y. Li, and L. A. DaSilva, “Resource reservation within sliced 5g networks: A cost-reduction strategy for service providers,” in Proceedings of the IEEE International Conference on Communications Workshops (ICC Workshops), June 2020, pp. 1–6. [44] M. Toscano, F. Grunwald, M. Richart, J. Baliosian, E. Grampín, and A. Castro, “Machine learning aided network slicing,” in Proceedings of the International Conference on Transparent Optical Networks (ICTON), July 2019, pp. 1–4. [45] S. Yang, X. Yu, , and Y. Zhou, “Lstm and gru neural network performance comparison study: Taking yelp review dataset as an example,” in Proceedings of the International Workshop on Electronics, Communications, and Artificial Intelligence (IWECAI), June 2020, pp. 98–101. [46] H. Chergui and C. Verikoukis, “Big data for 5g intelligent network,” IEEE Communications Letters, 2020. [47] L. Zanzi, J. X. Salvat, V. Sciancalepore, A. Garcia-Saavedra, and X. Costa-Perez, “Overbooking network slices end-to-end: Implementation and demonstration,” in Proc. of ACM SIGCOMM, 2018, p. 144–146. [48] J. X. Salvat, L. Zanzi, A. Garcia-Saavedra, V. Sciancalepore, and X. Costa-Perez, “Overbooking network slices through yield-driven end-to-end orchestration,” in Proc. Int. Conf. Emerging Networking EXperiments and Technologies (CoNEXT), 2018, p. 353–365. [49] F. Zarinni, A. Chakraborty, V. Sekar, S. R. Das, and P. Gill, “A first look at performance in mobile virtual network operators,” in Proceedings of the 2014 conference on internet measurement conference, ser. IMC ’14, 2014, pp. 165–172. [50] P. Schmitt, M. Vigil, and E. Belding, “A study of mvno data paths and performance,” in International Conference on Passive and Active Network Measurement. Springer, 2016, pp. 83–94. [51] A. Xiao, Y. Liu, Y. Li, F. Qian, Z. Li, S. Bai, Y. Liu, T. Xu, and X. Xin, “An in-depth study of commercial mvno: Measurement and optimization,” in Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services, ser. MobiSys ’19, 2019, pp. 457–468. [52] N. Vallina-Rodriguez, S. Sundaresan, C. Kreibich, N. Weaver, and V. Paxson, “Beyond the radio: Illuminating the higher layers of mobile networks,” in Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services, ser. MobiSys ’15, 2015, pp. 375–387. [53] Z. Yuan, Q. Li, Y. Li, S. Lu, C. Peng, and G. Varghese, “Resolving policy conflicts in multi-carrier cellular access,” in Proceedings of the 24th Annual International Conference on Mobile Computing and Networking, ser. MobiCom ’18. New York, NY, USA: Association for Computing Machinery, 2018, pp. 147–162. [Online]. Available: https://doi.org/10.1145/3241539.3241558 [54] A. Lutu, B. Jun, F. E. Bustamante, D. Perino, M. Bagnulo, and C. G. Bontje, “A first look at the ip exchange ecosystem,” SIGCOMM Comput. Commun. Rev., vol. 50, no. 4, p. 25?34, Oct. 2020. [Online]. Available: https://doi.org/10.1145/3431832.3431836 [55] E. Caushaj, I. Ivanov, H. Fu, I. Sethi, and Y. Zhu, “Evaluating throughput and delay in 3g and 4g mobile architectures,” Journal of Computer and Communications, vol. 02, pp. 1–8, 01 2014. [56] V. Vomhoff, M. Sichermann, S. Gieißler, A. Lutu, M. Giess, and T. Hoßfeld, “A shortcut through the ipx: Measuring latencies in global mobile roaming with regional breakouts,” in 2024 8th Network Traffic Measurement and Analysis Conference (TMA), 2024, pp. 1–10. [57] S. Geissler, F. Wamser, W. Bauer, M. Królikowski, S. Gebert, and T. Hossfeld, “Signaling traffic in internet-of-things mobile networks,” 2021 IFIP/IEEE International Symposium on Integrated Network Management (IM), pp. 452–458, 2021. [Online]. Available: https://api.semanticscholar.org/CorpusID:235420498 [58] A. M. Mandalari, A. Lutu, A. Custura, A. S. Khatouni, O. Alay, M. Bagnulo, V. Bajpai, A. Brunstrom, J. Ott, M. Trevisan, M. Mellia, and G. Fairhurst, “Measuring roaming in europe: Infrastructure and implications on users’ qoe,” IEEE Transactions on Mobile Computing, vol. 21, no. 10, pp. 3687–3699, 2022. [59] S. Sudhakaran, I. Ali, M. Eisen, J. Perez-Ramirez, C. Cazan, V. Frascolla, and D. Cavalcanti, “Zero-delay roaming for mobile robots enabled by wireless tsn redundancy,” in 2023 IEEE 19th International Conference on Factory Communication Systems (WFCS), 2023, pp. 1–8. [60] W. K. Saad, I. Shayea, B. J. Hamza, A. Azizan, M. Ergen, and A. Alhammadi, “Performance evaluation of mobility robustness optimization (mro) in 5g network with various mobility speed scenarios,” IEEE Access, vol. 10, pp. 60 955–60 971, 2022. [61] T. Sebastian, V. A. F. Rodriguez, Z. L. M. Moreira, and M. Guido, “Admission control and virtual network embedding in 5g networks: A deep reinforcement-learning approach,” IEEE Access, vol. 10, pp. 15 860–15 875, 2022. [62] J. J. Alves Esteves, A. Boubendir, F. Guillemin, and P. Sens, “A heuristically assisted deep reinforcement learning approach for network slice placement,” IEEE Transactions on Network and Service Management, vol. 19, no. 4, pp. 4794–4806, 2022. [63] D. Bega, M. Gramaglia, M. Fiore, A. Banchs, and X. Costa-Perez, “Deepcog: Cognitive network management in sliced 5g networks with deep learning,” in Proc. of IEEE International Conference on Computer Communications (IEEE INFOCOM), Paris, France, Apr. 2019, pp. 280–288. [64] J. A. Hurtado Sánchez, K. Casilimas, and O. M. Caicedo Rendon, “Deep reinforcement learning for resource management on network slicing: A survey,” Sensors, vol. 22, no. 8, 2022. [65] S. Troia, R. Alvizu, and G. Maier, “Reinforcement learning for service function chain reconfiguration in nfv-sdn metro-core optical networks,” IEEE Access, vol. 7, pp. 167 944– 167 957, 2019. [66] J. Koo, V. B. Mendiratta, M. R. Rahman, and A. Walid, “Deep reinforcement learning for network slicing with heterogeneous resource requirements and time varying traffic dynamics,” in Int. Conf. on Network and Service Management (CNSM), 2019, pp. 1–5. [67] Q. Liu, T. Han, and E. Moges, “Edgeslice: Slicing wireless edge computing network with decentralized deep reinforcement learning,” in IEEE Int. Conf. on Distributed Computing Systems (ICDCS), 2020, pp. 234–244. [68] The Linux Foundation, “The Linux Foundation and Google Cloud Launch Nephio to Enable and Simplify Cloud Native Automation of Telecom Network Functions,” Apr. 2022, consulted on March 10th 2023. [69] M. Burman and M. Gall, “Ericsson and Red Hat empower service providers to build multi-vendor networks,” Jun. 2022. [70] J. Rachid and J. Erfanian, “NGMN 5G initiative white paper,” Feb 2015. [71] S. D. A. Shah, M. A. Gregory, and S. Li, “Cloud-native network slicing using software defined networking based multi-access edge computing: A survey,” IEEE Access, vol. 9, pp. 10 903–10 924, 2021. [72] S. Arora and A. Ksentini, “Dynamic resource allocation and placement of cloud native network services,” in IEEE Int. Conf. Commun. (ICC), 2021, pp. 1–6. [73] A. Pino, P. Khodashenas, X. Hesselbach, E. Coronado, and S. Siddiqui, “Validation and benchmarking of cnfs in osm for pure cloud native applications in 5g and beyond,” in 2021 Int. Conf. on Computer Communications and Networks (ICCCN), 2021, pp. 1–9. [74] D. Giannopoulos, P. Papaioannou, C. Tranoris, and S. Denazis, “Monitoring as a service over a 5G network slice,” in Joint European Conf. on Networks and Commun. & 6G Summit (EuCNC/6G Summit), 2021, pp. 329–334. [75] X. Zhou, R. Li, T. Chen, and H. Zhang, “Network slicing as a service: enabling enterprises’ own software-defined cellular networks,” IEEE Communications Magazine, vol. 54, no. 7, pp. 146–153, 2016. [76] D. Bega, M. Gramaglia, A. B., V. Sciancalepore, K. Samdanis, and X. Costa-Perez, “Optimising 5g infrastructure markets: The business of network slicing,” in Proc. of IEEE INFOCOM, 2017, pp. 1–9. [77] K. T. Talluri and G. Van Ryzin, The theory and practice of revenue management. Springer, 2004, vol. 1. [78] C. Sexton, N. Marchetti, and L. A. DaSilva, “On provisioning slices and overbooking resources in service tailored networks of the future,” IEEE/ACM Transactions on Networking, vol. 28, no. 5, pp. 2106–2119, 2020. [79] S. Saxena and K. M. Sivalingam, “Slice admission control using overbooking for enhancing provider revenue in 5g networks,” in NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium, 2022, pp. 1–7. [80] S. Makridakis, E. Spiliotis, and V. Assimakopoulos, “The m4 competition: 100,000 time series and 61 forecasting methods,” Int. Journal of Forecasting, vol. 36, no. 1, pp. 54 – 74, 2020. [Online]. Available: http://www.sciencedirect.com/science/article/pii/ S0169207019301128 [81] L. L. Schiavo, M. Fiore, M. Gramaglia, A. Banchs, and X. Costa-Perez, “Forecasting for network management with joint statistical modelling and machine learning,” in 2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM). IEEE, 2022, pp. 60–69. [82] D. Tikunov and T. Nishimura, “Traffic prediction for mobile network using holt-winter’s exponential smoothing,” in Int. Conf. on Software, Telecommunications and Computer Networks. IEEE, 2007, pp. 1–5. [83] S. Martello and P. Toth, Knapsack Problems: Algorithms and Computer Implementations, ser. Wiley Series in Discrete Mathematics and Optimization. Wiley, 1990. [Online]. Available: https://books.google.es/books?id=0dhQAAAAMAAJ [84] S. Martello, “Knapsack Problems: Algorithms and Computer Implementations,” Wiley- Interscience series in discrete mathematics and optimiza tion, 1990. [85] F. Patzelt, “Colored noise,” https://github.com/felixpatzelt/colorednoise, 2022. [86] A. Garcia-Saavedra, X. Costa-Pérez, D. J. Leith, and G. Iosifidis, “FluidRAN: Optimized vRAN/MEC Orchestration,” in Proc. of IEEE International Conference on Computer Communications (IEEE INFOCOM), Honolulu, HI, USA, Apr. 2018, pp. 2366–2374. [87] J. Kim, D. Kim, and S. Choi, “3GPP SA2 architecture and functions for 5G mobile communication system,” ICT Express, vol. 3, no. 1, pp. 1–8, Mar. 2017. [88] L. Peterson, A. Al-Shabibi, T. Anshutz, S. Baker, A. Bavier, S. Das, J. Hart, G. Palukar, and W. Snow, “Central office re-architected as a datacenter,” IEEE Communication Magazine, vol. 54, no. 10, pp. 96–101, Oct. 2016. [89] J. Hao, T. Jiang, W. Wang, and I. K. Kim, “An empirical analysis of vm startup times in public iaas clouds,” in 2021 IEEE 14th International Conference on Cloud Computing (CLOUD), 2021, pp. 398–403. [90] 5G-CORAL, “Refined design of 5G-CORAL orchestration and control system and future directions,” Public Deliverable, D3.2, May 2019. [91] S. González, A. De la Oliva, C. J. Bernardos, and L. M. Contreras, “Towards a Resilient Openflow Channel Through MPTCP,” in Proc. of IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (IEEE BSMB), Valencia, Spain, Jun. 2018, pp. 1–5. [92] L. Zanzi, V. Sciancalepore, A. Garcia-Saavedra, and X. Costa-Pérez, “OVNES: Demonstrating 5G network slicing overbooking on real deployments,” in Proc. of IEEE International Conference on Computer Communications Workshops (IEEE INFOCOM WKSHPS), Honolulu, HI, USA, Apr. 2018, pp. 1–2. [93] W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes 3rd Edition: The Art of Scientific Computing, 3rd ed. USA: Cambridge University Press, 2007. [94] G. E. Dahl, T. N. Sainath, and G. E. Hinton, “Improving deep neural networks for LVCSR using rectified linear units and dropout,” in Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE ICASSP), Vancouver, BC, Canada, May 2013, pp. 8609–8613. [95] K. Janocha and W. M. Czarnecki, “On Loss Functions for Deep Neural Networks in Classification,” Schedae Informaticae, vol. 25, pp. 49–59, Mar. 2017. [96] Y. Gal and Z. Ghahramani, “Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning,” in Proc. of The 33rd International Conference on Machine Learning (ICML), New York, NY, USA, Jun. 2016, pp. 1050–1059. [97] M. J. Powell, “The BOBYQA algorithm for bound constrained optimization without derivatives,” Cambridge Technical Report NA2009/06, University of Cambridge, pp. 26–46, Aug. 2009. [98] W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes in FORTRAN 77: The Art of Scientific Computing. New York, NY, USA: Cambridge University Press, 1993, vol. 2. [99] J. Wang, J. Tang, Z. Xu, Y. Wang, G. Xue, X. Zhang, and D. Yang, “Spatiotemporal modeling and prediction in cellular networks: A big data enabled deep learning approach,” in IEEE INFOCOM 2017-IEEE conference on computer communications. IEEE, 2017, pp. 1–9. [100] G. Association, “LTE and EPC Roaming Guidelines,” https://www.gsma.com/newsroom/ wp-content/uploads/IR.88-v15.0.pdf, Nov. 2016, [Online; accessed 06-March-2018]. [101] A. M. Mandalari, A. Lutu, A. Custura, A. Safari Khatouni, O. Alay, M. Bagnulo, V. Bajpai, A. Brunstrom, J. Ott, M. Mellia, and G. Fairhurst, “Experience: Implications of roaming in europe,” in Proceedings of the 24th Annual International Conference on Mobile Computing and Networking, ser. MobiCom ’18. New York, NY, USA: Association for Computing Machinery, 2018, pp. 179–189. [Online]. Available: https://doi.org/10.1145/3241539.3241577 [102] T. Kugler, “Google Fi: Stay connected abroad with high speed data from Project Fi,” https: //www.blog.google/products/project-fi/stay-connected-abroad-with-high-speed/, accessed: 2021-12-10. [103] “Google Fi dialer codes,” https://arkienet.com/2018/01/google-fi-dialer-codes/, accessed: 2021-12-10. [104] “Twilio super sim,” https://www.twilio.com/docs/iot/supersim, accessed: 2021-12-10. [105] “Twilio super sim: Available networks,” https://www.twilio.com/docs/iot/supersim/ available-networks, accessed: 2021-12-10. [106] “Programmable wireless: Gain global cellular iot connectivity,” https://www.twilio.com/iot/ wireless, accessed: 2021-12-10. [107] M. Gharaibeh, A. Shah, B. Huffaker, H. Zhang, R. Ensafi, and C. Papadopoulos, “A look at router geolocation in public and commercial databases,” in Proceedings of the 2017 Internet Measurement Conference, ser. IMC ’17. New York, NY, USA: Association for Computing Machinery, 2017, p. 463–469. [Online]. Available: https://doi.org/10.1145/3131365.3131380 [108] A. Lutu, M. Trevisan, A. Safari Khatouni, A. M. Mandalari, A. Custura, M. Mellia, O. Alay, M. Bagnulo, V. Bajpai, A. Brunstrom, J. Ott, and G. Fairhurst, “Measuring Roaming in Europe: Infrastructure and Implications on Users QoE,” IEEE Transactions on Mobile Computing, pp. 1–1, 2021. [109] M. contributors, “Navigation timing api,” in MDN Web Docs, 2020, last accessed in 23/6/2021. [110] M. Rajiullah, A. Lutu, A. S. Khatouni, M.-R. Fida, M. Mellia, A. Brunstrom, O. Alay, S. Alfredsson, and V. Mancuso, “Web experience in mobile networks: Lessons from two million page visits,” in The World Wide Web Conference, 2019, pp. 1532–1543. [111] F. Wamser, M. Seufert, P. Casas, R. Irmer, P. Tran-Gia, and R. Schatz, “Yomoapp: A tool for analyzing qoe of youtube http adaptive streaming in mobile networks,” in 2015 European Conference on Networks and Communications (EuCNC). IEEE, 2015, pp. 239–243. [112] F. Li, A. A. Niaki, D. Choffnes, P. Gill, and A. Mislove, “A large-scale analysis of deployed traffic differentiation practices,” in Proceedings of the ACM Special Interest Group on Data Communication, 2019, pp. 130–144. [113] X. Foukas, N. Nikaein, M. M. Kassem, M. K. Marina, and K. Kontovasilis, “Flexran: A flexible and programmable platform for software-defined radio access networks,” in Proceedings of the 12th International on Conference on Emerging Networking EXperiments and Technologies, ser. CoNEXT ’16. New York, NY, USA: Association for Computing Machinery, 2016, p. 427?441. [Online]. Available: https://doi.org/10.1145/2999572.2999599 [114] X. Foukas, M. K. Marina, and K. Kontovasilis, “Orion: Ran slicing for a flexible and cost-effective multi-service mobile network architecture,” in Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking, ser. MobiCom ’17. New York, NY, USA: Association for Computing Machinery, 2017, p. 127?140. [Online]. Available: https://doi.org/10.1145/3117811.3117831 [115] I. Quintana-Ramirez, A. Tsiopoulos, M. A. Lema, F. Sardis, L. Sequeira, J. Arias, A. Raman, A. Azam, and M. Dohler, “The making of 5g: Building an end-to-end 5g-enabled system,” IEEE Communications Standards Magazine, vol. 2, no. 4, pp. 88–96, 2018. [116] “AWS global infrastructure,” https://aws.amazon.com/about-aws/global-infrastructure/, accessed: 2021-12-10. [117] “Open5gs v2.3.6,” https://github.com/open5gs/open5gs, accessed: 2021-12-10. [118] “Ueransim v3.2.4,” https://github.com/aligungr/UERANSIM, accessed: 2021-12-10. [119] L. Corneo, M. Eder, N. Mohan, A. Zavodovski, S. Bayhan, W. Wong, P. Gunningberg, J. Kangasharju, and J. Ott, Surrounded by the Clouds: A Comprehensive Cloud Reachability Study. New York, NY, USA: Association for Computing Machinery, 2021, p. 295–304. [Online]. Available: https://doi.org/10.1145/3442381.3449854 [120] “Amazon virtual private cloud,” https://docs.aws.amazon.com/vpc/latest/peering/ what-is-vpc-peering.html, accessed: 2021-12-10.es
dc.identifier.urihttps://hdl.handle.net/20.500.12761/1909
dc.description.abstractThe rapid transformation of mobile network infrastructures from hardware-based systems to software-defined networks (SDNs) has introduced several novel paradigms that aim to enhance scalability, flexibility, and efficiency. One of the most significant advancements in this shift is the concept of network slicing. In essence, network slicing allows a single physical network to be divided into multiple virtual networks, each tailored to the specific requirements of different use cases. These slices can vary in terms of bandwidth, latency, security, and resource allocation, making them highly adaptable to different industries, such as autonomous vehicles, smart cities, and Internet of Things (IoT) applications. This capability offers unprecedented flexibility for service providers, as they can deploy and manage various services on the same physical infrastructure without the need for expensive and complex hardware upgrades. However, realizing the full potential of network slicing presents several challenges. One of the key issues is the complex management of multiple network slices that may have conflicting requirements. For example, ensuring ultra-reliable low-latency communication for one slice while maximizing bandwidth for another slice can be difficult, especially when they share the same underlying physical resources. The dynamic nature of network slicing, where slices are created, modified, and terminated on demand, also requires highly efficient orchestration and automation systems. Furthermore, maintaining security and isolation between slices is essential to prevent interference or data breaches. As mobile networks continue to evolve, addressing these challenges will be crucial to fully unlocking the benefits of network slicing and realizing the vision of highly flexible, software-driven networks. This thesis explores innovative approaches to improve the profitability of Mobile Network Operators (MNO) through advanced network slicing strategies in 5G networks. The research focuses on three key areas: overbooking network slices for maximize financial gains, cost-efficient network slice management, and operational performance of Mobile Network Aggregators (MNA) within Network Slicing as a Service (NSaaS) context under roaming scenarios. These innovations leverage state-of-the-art techniques, including deep learning, classical optimization, and data-driven algorithms, to tackle the complex challenges of resource provisioning, network function lifecycle management, and global service optimization. This document delves into these advancements, structured across several chapters that explore these key areas in depth. In the first contribution, we explore the NSaaS concept and introduce slice overbooking as a promising strategy to maximize resource utilization and boost net profit in cloud-native mobile networks. Network slicing enables the creation of virtualized, custom-tailored network slices, but managing these slices efficiently remains a challenge. Overbooking allows network operators to admit more slices than available physical resources by capitalizing on the fact that tenants seldom use their total reserved capacity simultaneously. The chapter presents a complete NSaaS management solution, overbooKing-Aware Network Slicing as a Service (kaNSaaS), which integrates deep learning with classical optimization to address the dual problems of admission control and resource allocation. Through extensive experimentation with large-scale real-world data on tenant demands, the results show that kaNSaaS boosts operator profits potentially multiplying it by four compared to non-overbooking strategies under real-world conditions. In the second contribution, the focus shifts to zero-touch management systems, which promise autonomous network operation with minimal human intervention. As modern network infrastructures have evolved into systems with numerous virtualized network functions, traditional manual approaches to lifecycle management are increasingly inadequate. In response, this chapter introduces AZTEC+, a data-driven solution for anticipatory resource provisioning in network slicing environments. Using a hybrid and modular deep learning architecture, AZTEC+ forecasts future service demands and determines optimal trade-offs between resource provisioning, instantiation, and reconfiguration costs and performance requirements. Tested on a large-scale network, AZTEC+ outperformed existing state-of-the-art management strategies by up to 5.85 times, proving its effectiveness in reducing network costs and addressing the complexity of virtualized mobile networks. It effectively balances costs associated with resource instantiation and reconfiguration, making it a highly efficient solution for managing dynamic network slices autonomously. This chapter emphasizes how zero-touch management, paired with anticipatory resource provisioning, offers a scalable approach to future network management. In the third contribution, we explore the added complexity introduced by MNAs, which represent a new frontier in global mobile telecommunications in NSaaS. MNAs, such as Google Fi, Twilio, and Truphone, operate by leveraging multiple MNOs to provide mobile communication services across different regions. Unlike traditional MNOs, which are limited by geographic boundaries, MNAs dynamically connect to the MNO that offers the best performance based on location and time, ensuring optimal service quality for users, especially those frequently crossing borders. However, the dynamic nature of MNAs introduces a new layer of complexity in meeting network slicing’s Quality of Service (QoS) guarantees as the isolation and management of slices become more intricate with the involvement of multiple, regionally dispersed MNOs. To address this, we quantify and compare the performance of MNA-driven models against traditional MNOs, offering insights into the challenges and trade-offs in achieving reliable QoS in these advanced operator frameworks. This section presents a detailed performance analysis of the three aforementioned MNAs, comparing their performance for key applications like web browsing and video streaming within NSaaS across diverse geographical regions, namely the USA and Spain. While MNAs may introduce slight delays compared to local MNOs in certain regions, they significantly outperform the traditional home-routed roaming model in terms of service quality. Moreover, emulation studies using open-source 5G implementations deployed across Amazon Web Services (AWS) locations illustrate the performance gains MNAs can achieve through advanced network function virtualization. This chapter highlights the potential of the MNA model to reshape global mobile services, offering more flexible, efficient, and seamless experiences for end-users. Overall, this research provides valuable insights into NSaaS overbooking management, zero-touch resource provisioning, and the global reach of MNAs. Together, these innovations represent significant strides toward realizing the next generation of mobile networks, where resource efficiency, automation, and global service quality are paramount. The research highlights the economic benefits of slice overbooking, demonstrating how operators can significantly increase profitability by intelligently managing their resources. Furthermore, by integrating hybrid models of AI and optimization, it lays a strong foundation for future developments in network slicing and cost optimization, offering practical implications for both MNO and application developers. As 5G networks continue to evolve, this work sheds light on the shifting landscape of global network operators and provides a roadmap for addressing the growing complexities of resource allocation and service management in a cloud-native, AI-driven environment. Through a blend of deep learning, anticipatory algorithms, and network virtualization, the telecommunications industry is better positioned to meet the ever-evolving demands of users while optimizing network performance across a wide range of dynamic environments.es
dc.description.sponsorshipHorizon 2020es
dc.language.isoenges
dc.titleEfficient 5G Mobile Network Management: Network Slicing and Global Roaming Optimizationes
dc.typedoctoral thesises
dc.type.hasVersionAOes
dc.rights.accessRightsopen accesses
dc.description.departmentTelematics Engineeringes
dc.description.institutionUniversidad Carlos III de Madrid, Spaines
dc.page.total136es


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