A Hierarchical Buffer-Sizing Framework for Congestion Mitigation in Campus Area Networks: An Engineering-Theoretic Approach to the Internet Sluggishness Problem
DOI:
https://doi.org/10.56313/jictas.v5i1.532Keywords:
Campus Area Networks Buffer Sizing, Network Congestion Queuing Delay, Hierarchical Network Architecture, Switched LAN Internet Sluggishness, Graph-Theoretic Network DesignAbstract
Campus Area Networks (CANs) in higher educational institutions worldwide have long suffered from Internet sluggishness—a persistent degradation in upload and download throughput that occurs at predictable temporal intervals. Prior investigations have narrowly attributed this phenomenon to insufficient bandwidth, recommending either bandwidth overprovisioning or policy-based management without resolving the problem. This paper advances a fundamentally different, engineering-theoretic explanation and a novel quantitative resolution: the sluggishness is primarily caused by the uniform deployment of identically buffered switches across all hierarchical layers of the network, which violates the traffic aggregation principle intrinsic to layered switched architectures. Using a physically installed university CAN at Afe Babalola University, Ado-Ekiti, Nigeria, we formally derive a Hierarchical Buffer-Sizing (HBS) framework grounded in graph-theoretic tree analysis. The proposed HBS framework yields per-switch buffer size specifications as a function of each switch's subtree cardinality within the network topology. Results show that the required buffer capacity for core-layer switches can be up to 14× greater than that of edge-layer leaf switches, a disparity completely absent in existing installations. Comparative simulation using NS-3 demonstrates that networks configured according to the HBS framework reduce average end-to-end queuing delay by 68.4% and packet drop rate by 73.1% relative to uniform-buffer baselines. The framework is analytically validated against both the small-buffer model of Appenzeller et al. [4] and the very-small-buffer model of Enachescu et al. [6], with all derived buffer values falling within theoretically acceptable bounds. This work provides, for the first time, a deterministic, topology-driven engineering methodology for CAN buffer provisioning that can be directly implemented by network engineers without traffic monitoring prerequisites
References
M. Eyinagho and O. Emoruoa, “On The Last Mile Campus Area Networks’ Aspect Of The Internet Sluggishness Problem,” Journal of ICT Aplications and System, p., 2025, doi: 10.56313/jictas.v4i2.458.
H. Kusbandono, T. Lestariningsih, and T. Septianto, “Comparative Analysis of Quality of Service (QoS) on WLAN Network Bandwidth Management using HTB Method with PCQ,” East Asian Journal of Multidisciplinary Research, p., 2024, doi: 10.55927/eajmr.v3i10.11675.
P. Pranshul and S. S. Kang, “Congestion Control Approach in Software Defined Network Using Buffer Size Parameter,” in 2023 Global Conference on Information Technologies and Communications (GCITC), 2023, pp. 1–7. doi: 10.1109/gcitc60406.2023.10426261.
K. S. Beni, M. Soltanaghaei, and R. Sadeghi, “A Hybrid Active Queue Management Algorithm for Packet Management in Software Defined Networking,” Concurr. Comput., vol. 37, p., 2025, doi: 10.1002/cpe.70239.
A. Abdelmoniem and B. Bensaou, “Alleviating Congestion via Switch Design for Fair Buffer Allocation in Datacenters,” IEEE Transactions on Cloud Computing, vol. 12, pp. 219–231, 2024, doi: 10.1109/tcc.2024.3357595.
K. Agrawal, V. Addanki, and H. Mostafaei, Dequeue Rate-Agnostic Switch Buffer Sharing through Packet Queueing Delay. 2024. doi: 10.1145/3694812.3699924.
B. Yanto, B. Basorudin, S. Anwar, A. Lubis, and K. Karmi, “Smart Home Monitoring Pintu Rumah Dengan Identifikasi Wajah Menerapkan Camera ESP32 Berbasis IoT,” Jurnal Sisfokom (Sistem Informasi dan Komputer), vol. 11, no. 1, 2022, doi: 10.32736/sisfokom.v11i1.1180.
R. Agrawal, H. Sarkar, A. O. Prasad, A. K. Sahoo, A. Vidyarthi, and R. K. Barik, “Exploration of Deep Neural Networks and Effect of Optimizer for Pulmonary Disease Diagnosis,” SN Comput. Sci., vol. 4, no. 5, 2023, doi: 10.1007/s42979-023-01940-9.
H. Z. Yuan, K. H. Ghazali, A. Lubis, S. Sunardi, and B. Yanto, “Implementing Image Processing for Quality Inspection of Car Air Conditioning Vents †,” 2025.
T. S. J. Darwish et al., “An intelligent traffic and vehicle monitoring system using internet of things architecture,” IEEE Access, vol. 11, no. 4, 2023.
N. Bouacida and B. Shihada, “Practical and Dynamic Buffer Sizing Using LearnQueue,” IEEE Trans. Mob. Comput., vol. 18, pp. 1885–1897, 2019, doi: 10.1109/tmc.2018.2868670.
B. Spang, S. Arslan, and N. McKeown, “Updating the Theory of Buffer Sizing,” ACM SIGMETRICS Performance Evaluation Review, vol. 49, pp. 55–56, 2021, doi: 10.1145/3529113.3529131.
M. Hao, M. Cai, M. Fang, and L. You, “SiG: A Siamese-Based Graph Convolutional Network to Align Knowledge in Autonomous Transportation Systems,” ACM Trans. Intell. Syst. Technol., vol. 15, no. 2, 2024, doi: 10.1145/3643861.
C. You, Y. Zhao, G. Feng, T. Quek, and L. M. Li, “Hierarchical Multiresource Fair Queueing for Packet Processing,” IEEE Transactions on Network and Service Management, vol. 20, pp. 726–740, 2022, doi: 10.1109/tnsm.2022.3197747.
X. Huang et al., “Clean: Minimize Switch Queue Length via Transparent ECN-proxy in Campus Networks,” 2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS), pp. 1–6, 2021, doi: 10.1109/iwqos52092.2021.9521295.
M. Eyinagho and S. Falaki, “Appropriate buffer sizes for Internet nodal devices: A networks’ topologies–based solution,” International Journal of Communication Systems, vol. 33, p., 2020, doi: 10.1002/dac.4359.
C. Pan, Y. Wang, H. Shi, and W. Tian, “DRL-ABS: Deep Reinforcement Learning-Based Adaptive Buffer Sizing for Edge Routers in AIoT Networks,” IEEE Internet Things J., vol. 12, pp. 30475–30492, 2025, doi: 10.1109/jiot.2025.3572481.
A. Showail, K. Jamshaid, and B. Shihada, “Buffer sizing in wireless networks: challenges, solutions, and opportunities,” IEEE Communications Magazine, vol. 54, pp. 130–137, 2016, doi: 10.1109/mcom.2016.7452277.
A. Dhamdhere, H. Jiang, and C. Dovrolis, “Buffer sizing for congested Internet links,” in Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies., 2005, pp. 1072–1083. doi: 10.1109/infcom.2005.1498335.
S. Hou, Y. Hu, L. Tian, and P. Cui, “Poly-HQoS: a polymorphic packet scheduler for traffic isolation in multi-tenant cloud environment,” Journal of King Saud University Computer and Information Sciences, vol. 37, p., 2025, doi: 10.1007/s44443-025-00002-9.
M. Irazabal, E. López-Aguilera, I. Demirkol, and N. Nikaein, “Dynamic Buffer Sizing and Pacing as Enablers of 5G Low-Latency Services,” IEEE Trans. Mob. Comput., vol. 21, pp. 926–939, 2022, doi: 10.1109/tmc.2020.3017011.
L. Zheng, Z. Qiu, S. Sun, W. Pan, Y. Gao, and Z. Zhang, “Design and analysis of a parallel hybrid memory architecture for per-flow buffering in high-speed switches and routers,” Journal of Communications and Networks, vol. 20, pp. 578–592, 2018, doi: 10.1109/jcn.2018.000090.
Published
How to Cite
Issue
Section
Copyright (c) 2026 Journal of ICT Aplications and System

This work is licensed under a Creative Commons Attribution 4.0 International License.

