Mathematical Modeling for Network Upgrades in Internet Service Provider Infrastructure
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Keywords:
Mathematical Modeling, Network Performance, Network Upgrades, Bandwidth Utilization, Scalability AnalysisAbstract
The ongoing growth of the need for superior Internet services creates great pressure on the ISPs
as to the accurate estimation of network upgrade need. The following work introduces a mathematical
modeling methodology that can assist in assessing network performance and identifying scenarios that
require additional investment in network facilities. Bandwidth usage, server load, delay factors and
throughput are evaluated for estimating the effect of different traffic conditions on the effectiveness of the
network. This paper’s simulations prove the model’s usefulness in pinpointing such thresholds so that ISPs
can prepare their upgrades and prevent performance constrictions on schedule. The results raise awareness
of how higher resource efficiency and service provision can be attained through focused resource
management and prevention or risk planning. This studies’ contribution, thus, is in offering mathematical
solutions to the ISPs that they can deploy to manage their operations, address the dynamic customer
expectations, and deal with the complexities that continue to characterize the digital environment.
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References
K. Salah, K. Elbadawi, and R. Boutaba, “Performance modeling and analysis of network firewalls,” IEEE Transactions on Network and Service Management, vol. 9, no. 1, pp. 12–21, Mar. 2012, doi: 10.1109/TNSM.2011.122011.110151.
F. Carpio, W. Bziuk, and A. Jukan, “Scaling Migrations and Replications of Virtual Network Functions based on Network Traffic Forecasting,” May 2021, doi: 10.1016/j.comnet.2021.108582.
A. Downey, E. Tuna, and A. Soysal, “An Adaptive Framework for Generalizing Network Traffic Prediction towards Uncertain Environments,” Nov. 2023, [Online]. Available: http://arxiv.org/abs/2311.18824
L. Hui, M. Wang, L. Zhang, L. Lu, and Y. Cui, “Digital Twin for Networking: A Data-driven Performance Modeling Perspective,” Jun. 2022, [Online]. Available: http://arxiv.org/abs/2206.00310
A. Liu, S. Liu, and X. Pei, “Deeper and Wider Networks for Performance Metrics Prediction in Communication Networks,” Dec. 2023, [Online]. Available: http://arxiv.org/abs/2401.00429
F. Le, M. Srivatsa, R. Ganti, and V. Sekar, “Rethinking Data-driven Networking with Foundation Models: Challenges and Opportunities,” in HotNets 2022 - Proceedings of the 2022 21st ACM Workshop on Hot Topics in Networks, Association for Computing Machinery, Inc, Nov. 2022, pp. 188–197. doi: 10.1145/3563766.3564109.
M. Ferriol-Galmes et al., “RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation,” Proceedings - IEEE INFOCOM, vol. 2022-May, pp. 2018–2027, 2022, doi: 10.1109/INFOCOM48880.2022.9796944.
K. Shen and B. Li, “Learning-Based Network Performance Estimators: The Next Frontier for Network Simulation,” IEEE Netw, vol. 37, no. 4, pp. 97–103, Jul. 2023, doi: 10.1109/MNET.013.2300053.
L. Hui, M. Wang, L. Zhang, L. Lu, and Y. Cui, “Digital Twin for Networking: A Data-Driven Performance Modeling Perspective,” IEEE Netw, vol. 37, no. 3, pp. 202–209, May 2023, doi: 10.1109/MNET.119.2200080.
M. Skocaj et al., “Data-driven Predictive Latency for 5G: A Theoretical and Experimental Analysis Using Network Measurements,” Jul. 2023, doi: 10.1109/PIMRC56721.2023.10293861.
M. Malekzadeh, “Performance prediction and enhancement of 5G networks based on linear regression machine learning,” EURASIP J Wirel Commun Netw, vol. 2023, no. 1, Dec. 2023, doi: 10.1186/s13638-023-02282-z.
H. Shin, S. Oh, A. Isah, I. Aliyu, J. Park, and J. Kim, “Network Traffic Prediction Model in a Data-Driven Digital Twin Network Architecture,” Electronics (Switzerland), vol. 12, no. 18, Sep. 2023, doi: 10.3390/electronics12183957.
M. T. Arashloo, R. Beckett, and R. Agarwal, “Formal Methods for Network Performance Analysis,” in 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23), Boston, MA: USENIX Association, Apr. 2023, pp. 645–661. [Online]. Available: https://www.usenix.org/conference/nsdi23/presentation/tahmasbi
M. Qin, “Towards a Unified Method for Network Dynamic via Adversarial Weighted Link Prediction,” Jan. 2024, [Online]. Available: http://arxiv.org/abs/2401.03444
H. Nan, R. Li, X. Zhu, J. Ma, and D. Niyato, “An Efficient Data-Driven Traffic Prediction Framework for Network Digital Twin,” IEEE Netw, vol. 38, no. 1, pp. 22–29, Jan. 2024, doi: 10.1109/MNET.2023.3335952.
M. Panek, I. Jabłoński, and M. Woźniak, “Modeling configuration-performance relation in a mobile network: a data-driven approach,” Jul. 2024, [Online]. Available: http://arxiv.org/abs/2407.06702
H. M. Mohammed and Q. I. Ali, “E-proctoring systems: A review on designing techniques, features and abilities against threats and attacks,” Quantum Journal of Engineering, Science and Technology, vol. 3, no. 2, pp. 14–30, 2022.
Q. I. Ali, “Design, implementation & optimization of an energy harvesting system for VANETS’road side units (RSU)’,” IET Intell. Transp. Syst, vol. 8, no. 3, pp. 298–307, 2014.
S. Lazim Qaddoori and Q. I. Ali, “An embedded and intelligent anomaly power consumption detection system based on smart metering,” IET Wireless Sensor Systems, vol. 13, no. 2, pp. 75–90, 2023.
S. L. Qaddoori and Q. I. Ali, “An efficient security model for industrial internet of things (IIoT) system based on machine learning principles,” Al-Rafidain Engineering Journal (AREJ), vol. 28, no. 1, pp. 329–340, 2023.
