Interactive Client-Side Network Simulation with Dynamic Bandwidth Management and Real-Time 3D Visualization
DOI:
https://doi.org/10.33998/processor.2025.20.2.2500Keywords:
Bandwidth Management, Client-side Rendering, Data Visualization, Network Simulation, Three.jsAbstract
Effective network management in dynamic environments necessitates robust tools for real-time analysis and visualization. This paper presents a client-side network simulation designed to model and visualize complex network behaviors, including dynamic bandwidth allocation, traffic spillover across multiple Internet Service Providers (ISPs), and system-wide congestion throttling. Leveraging Three.js, the system procedurally generates a 25-node network topology and simulates fluctuating traffic demands using a stochastic model. A central router implements a hierarchical spillover logic for external traffic and a proportional throttling algorithm to cap total bandwidth, ensuring network stability under peak loads. Real-time data visualization is achieved through animated data packets, color-coded node states, and an overlaid 2D time-series graph. Validation confirms the system's functional validity, demonstrating its potential as an educational tool and a proof-of-concept for sophisticated digital twins for network operations.
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M. Soori, B. Arezoo, and R. Dastres, “Internet of things for smart factories in industry 4.0, a review,” Internet Things
Cyber-Phys. Syst., vol. 3, pp. 192–204, 2023, doi: 10.1016/j.iotcps.2023.04.006.
A. Mitropoulou, P. Kokkinos, and E. Varvarigos, “Identifying Network Congestion Using Knowledge Graphs and Link
Prediction,” in Proceedings of the IEEE/ACM 16th International Conference on Utility and Cloud Computing, Taormina
(Messina) Italy: ACM, Dec. 2023, pp. 1–6. doi: 10.1145/3603166.3632129.
V. Filipov, A. Arleo, and S. Miksch, “Are We There Yet? A Roadmap of Network Visualization from Surveys to Task
Taxonomies,” Comput. Graph. Forum, vol. 42, no. 6, p. e14794, Sep. 2023, doi: 10.1111/cgf.14794.
H. Guo and L. Xu, “Research on the application of big data visualization technology in urban road congestion,” Eur. J.
Remote Sens., vol. 56, no. 1, p. 2147448, Dec. 2023, doi: 10.1080/22797254.2022.2147448.
Y. Xin, S. Sasidharam, C. Wang, and M. Cevik, “Taming Imbalance and Complexity in WAN Traffic Engineering,”
Dec. 23, 2024, arXiv: arXiv:2412.17248. doi: 10.48550/arXiv.2412.17248.
S. H. Yajadda and F. Safaei, “A Novel Reinforcement Learning Routing Algorithm for Congestion Control in Complex
Networks,” Dec. 30, 2023, arXiv: arXiv:2401.00297. doi: 10.48550/arXiv.2401.00297.
C. Güemes-Palau, M. Ferriol-Galmés, J. Paillisse-Vilanova, A. López-Brescó, P. Barlet-Ros, and A. Cabellos-Aparicio,
“RouteNet-Gauss: Hardware-Enhanced Network Modeling with Machine Learning,” Jan. 15, 2025, arXiv:
arXiv:2501.08848. doi: 10.48550/arXiv.2501.08848.
Muhammad Shafiq, Muhammad Adnan Sami, Nudrat Bano, Rida Bano, and Muhammad Rashid, “Artificial Intelligence
in Physics Education: Transforming Learning from Primary to University Level,” Indus J. Soc. Sci., vol. 3, no. 1, pp.
–733, Mar. 2025, doi: 10.59075/ijss.v3i1.807.
M. A. Kamal, H. W. Raza, M. M. Alam, M. M. Su’ud, and A. B. A. B. Sajak, “Resource Allocation Schemes for 5G
Network: A Systematic Review,” Sensors, vol. 21, no. 19, p. 6588, Oct. 2021, doi: 10.3390/s21196588.
E. Friday, A. Olatunji, and A. Oluwatosin, “Cloud-Based Resource Management for Scalable Application Deployment,”
Int. J. Wirel. Commun. Mob. Comput., vol. 12, no. 1, pp. 1–15, Feb. 2025, doi: 10.11648/j.wcmc.20251201.11.
E. Baydal, P. Lopez, and J. Duato, “A family of mechanisms for congestion control in wormhole networks,” IEEE
Trans. Parallel Distrib. Syst., vol. 16, no. 9, pp. 772–784, Sep. 2005, doi: 10.1109/TPDS.2005.102.
M. Thottethodi, A. R. Lebeck, and S. S. Mukherjee, “Exploiting global knowledge to achieve self-tuned congestion
control for k-ary n-cube networks,” IEEE Trans. Parallel Distrib. Syst., vol. 15, no. 3, pp. 257–272, Mar. 2004, doi:
1109/TPDS.2004.1264810.
A. Komathi et al., “Network load balancing and data categorization in cloud computing,” Indones. J. Electr. Eng.
Comput. Sci., vol. 35, no. 3, p. 1942, Sep. 2024, doi: 10.11591/ijeecs.v35.i3.pp1942-1951.
M. Shuaib et al., “An Optimized, Dynamic, and Efficient Load-Balancing Framework for Resource Management in the
Internet of Things (IoT) Environment,” Electronics, vol. 12, no. 5, p. 1104, Feb. 2023, doi:
3390/electronics12051104.
A. A. Abu-Shareha, M. M. Abualhaj, A. Alshahrani, and B. Al-Kasasbeh, “A four-state Markov model for modelling
bursty traffic and benchmarking of random early detection,” Int. J. Data Netw. Sci., vol. 8, no. 2, pp. 1151–1160, 2024,
doi: 10.5267/j.ijdns.2023.11.019.
N. Hohn, D. Veitch, and P. Abry, “Cluster processes: a natural language for network traffic,” IEEE Trans. Signal
Process., vol. 51, no. 8, pp. 2229–2244, Aug. 2003, doi: 10.1109/TSP.2003.814460.
Z. Li, M. W. Levin, X. Qu, and R. Stern, “A Network Traffic Model for the Control of Autonomous Vehicles Acting as
Moving Bottlenecks,” IEEE Trans. Intell. Transp. Syst., vol. 24, no. 9, pp. 9004–9015, Sep. 2023, doi:
1109/TITS.2023.3271187.
X. Simon Zhou et al., “A meso-to-macro cross-resolution performance approach for connecting polynomial arrival
queue model to volume-delay function with inflow demand-to-capacity ratio,” Multimodal Transp., vol. 1, no. 2, p.
, Jun. 2022, doi: 10.1016/j.multra.2022.100017.
V. N. Skoropad et al., “Dynamic Traffic Flow Optimization Using Reinforcement Learning and Predictive Analytics:
A Sustainable Approach to Improving Urban Mobility in the City of Belgrade,” Sustainability, vol. 17, no. 8, p. 3383,
Apr. 2025, doi: 10.3390/su17083383.
C. Kleinbeck, H. Schieber, K. Engel, R. Gutjahr, and D. Roth, “Multi-Layer Gaussian Splatting for Immersive Anatomy
Visualization,” IEEE Trans. Vis. Comput. Graph., vol. 31, no. 5, pp. 2353–2363, May 2025, doi:
1109/TVCG.2025.3549882.
W. K. Monib, A. Qazi, R. A. Apong, and M. M. Mahmud, “Investigating Learners’ Perceptions of Microlearning:
Factors Influencing Learning Outcomes,” IEEE Access, vol. 12, pp. 178251–178266, 2024, doi:
1109/ACCESS.2024.3472113.
L. Fang, “The Impact of AI Tools on ESL Learners’ Engagement and Language Learning Motivation,” J. Educ. Educ.
Res., vol. 12, no. 3, pp. 111–114, Mar. 2025, doi: 10.54097/hvm6w044.
L. Li, X. Qiao, Q. Lu, P. Ren, and R. Lin, “Rendering Optimization for Mobile Web 3D Based on Animation Data
Separation and On-Demand Loading,” IEEE Access, vol. 8, pp. 88474–88486, 2020, doi:
1109/ACCESS.2020.2993613.
M. A. AboArab et al., “DECODE-3DViz: Efficient WebGL-Based High-Fidelity Visualization of Large-Scale Images
using Level of Detail and Data Chunk Streaming,” J. Imaging Inform. Med., Feb. 2025, doi: 10.1007/s10278-025-01430-
M. K. Ikhwan and K. B. Yogha Bintoro, “Learning Automata Based-AODV Routing Protocol for Inter-vehicle
Communication: A Simulation Approach,” processor, vol. 20, no. 1, May 2025, doi:
33998/processor.2025.20.1.2209.
E. Guler, “CITE-PSO: Cross-ISP Traffic Engineering Enhanced by Particle Swarm Optimization in Blockchain Enabled
SDONs,” IEEE Access, vol. 12, pp. 27611–27632, 2024, doi: 10.1109/ACCESS.2024.3367600.
C. Liu, A. Chakraborty, N. Chawla, and N. Roggel, “Frequency Throttling Side-Channel Attack,” in Proceedings of the
ACM SIGSAC Conference on Computer and Communications Security, Los Angeles CA USA: ACM, Nov. 2022,
pp. 1977–1991. doi: 10.1145/3548606.3560682.
S. Narayan Chattopadhyay and A. Kumar Gupta, “Tipping points, multistability, and stochasticity in a two-dimensional
traffic network dynamics,” Chaos Interdiscip. J. Nonlinear Sci., vol. 34, no. 7, p. 073107, Jul. 2024, doi:
1063/5.0202785.
S. LYi, Q. Wang, F. Lekschas, and N. Gehlenborg, “Gosling: A Grammar-based Toolkit for Scalable and Interactive
Genomics Data Visualization,” IEEE Trans. Vis. Comput. Graph., vol. 28, no. 1, pp. 140–150, Jan. 2022, doi:
1109/TVCG.2021.3114876.
S. Di Bartolomeo, M. Riedewald, W. Gatterbauer, and C. Dunne, “STRATISFIMAL LAYOUT: A modular
optimization model for laying out layered node-link network visualizations,” IEEE Trans. Vis. Comput. Graph., vol. 28,
no. 1, pp. 324–334, Jan. 2022, doi: 10.1109/TVCG.2021.3114756.
A. Bauerle, C. Van Onzenoodt, and T. Ropinski, “Net2Vis – A Visual Grammar for Automatically Generating
Publication-Tailored CNN Architecture Visualizations,” IEEE Trans. Vis. Comput. Graph., vol. 27, no. 6, pp. 2980–
, Jun. 2021, doi: 10.1109/TVCG.2021.3057483.
Hendri, “Optimisasi Pembelajaran Online di MTS Al Falah menggunakan Node.js Express dan MongoDB,” processor,
vol. 18, no. 2, Nov. 2023, doi: 10.33998/processor.2023.18.2.831.
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