Distributed Congestion Control Based on Utility Function





Congestion Control, Utility Function, Real-Time applications, Elastic Applications, Distributed Optimization, Proactive Algorithm.


This paper introduces the Distributed Utility Function Algorithm (D-AFU) as a notable progression in managing and optimizing network traffic within distributed settings. Based on the utility function principle, D-AFU dynamically adjusts data rate in response to ever-changing network demands, with optimal performance and a higher user experience. Contrary to the centralized model, D-AFU employs a distributed, scalable, and resilient against failures and system overloads mechanism. Its efficiency is validated using the NS-3 simulator. Three main metrics were used: the data rate allocation, utility per session, and fairness (quantified by the Gini coefficient). D-AFU displays exceptional performance and low latency, particularly vital for real-time applications with high Quality of Service (QoS) requirements.



Download data is not yet available.


Cisco, “Cisco Annual Internet Report (2018–2023),” 2020. [Online]. Available: https://www.cisco.com/c/dam/en/us/solutions/collateral/executive-perspectives/annual-internet-report/white-paper-c11-741490.docx/jcr%3acontent/renditions/white-paper-c11-741490_0.png

J. F. Kurose and K. W. Ross, Computer Networking: A Top-down Approach. Pearson, 2017. [Online]. Available: https://books.google.com.ec/books?id=OljpOAAACAAJ

W. R. Stevens and G. R. Wright, TCP/IP Illustrated: The Protocols, ser. Addison-Wesley professional computing series. Addison-Wesley, 1994. [Online]. Available: https://books.google.com.ec/books?id=-btNds68w84C

R. Buyya, C. S. Yeo, and S. Venugopal, “Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities,” in Proceedings - 10th IEEE International Conference on High Performance Computing and Communications, HPCC 2008. IEEE, 2008, pp. 5–13.

P. Ludeña, “Fairness and Proactive Congestion Control in Multipath Networks,” Ph.D. dissertation, Universidad Politécnica de Madrid, 2021.

M. Alizadeh, A. Greenberg, D. A. Maltz, J. Padhye, P. Patel, B. Prabhakar, S. Sengupta, and M. Sridharan, “Data Center TCP (DCTCP),” SIGCOMM Comput. Commun. Rev., vol. 40, no. 4, pp. 63–74, 2010. [Online]. Available: https://doi.org/10.1145/1851275.1851192

K. R. Fall and S. Floyd, “Simulation-Based Comparisons of Tahoe, Reno and SACK TCP,” Comput. Commun. Rev., vol. 26, pp. 5–21, 1996. [Online]. Available: https://api.semanticscholar.org/CorpusID:7459148

P. Ludeña-González, J. L. López-Presa, and F. D. Muñoz, “Upward Max-Min Fairness in Multipath High-Speed Networks,” IEEE Access, 2021.

N. Li, Z. Deng, Q. Zhu, and Q. Du, “AdaBoost-TCP: A Machine Learning-Based Congestion Control Method for Satellite Networks,” in 2019 IEEE 19th International Conference on Communication Technology (ICCT), 2019, pp. 1126–1129.

S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge University Press, 2004. [Online]. Available: https://books.google.com.ec/books?id=IUZdAAAAQBAJ&printsec=frontcover&hl=es&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false

M. Bahnasy, H. Elbiaze, and B. Boughzala, “Zero-Queue Ethernet Congestion Control Protocol Based on Available Bandwidth Estimation,” Journal of Network and Computer Applications, vol. 105, pp. 1–20, 2018.

R. Adams, “Active Queue Management: A Survey,” IEEE Communications Surveys and Tutorials, vol. 15, no. 3, pp. 1425–1476, 2013.

S. Varma, Internet Congestion Control. Elsevier Science, 2015. [Online]. Available: https://books.google.com.ec/books?id=gbPoBgAAQBAJ&printsec=frontcover&hl=es&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false

S. Hu, W. Bai, B. Qiao, K. Chen, and K. Tan, “Augmenting Proactive Congestion Control with AEOLUs,” in ACM International Conference Proceeding Series, 2018, pp. 22–28.

T. Issariyakul and E. Hossain, Introduction to Network Simulator NS2. Springer US, 2009.

D. Giannopoulos, N. Chrysos, E. Mageiropoulos, G. Vardas, L. Tzanakis, and M. Katevenis, “Accurate Congestion Control for RDMA Transfers,” 2018 Twelfth IEEE/ACM International Symposium on Networks-on-Chip (NOCS), pp. 1–8, 2018.

M. Bahnasy and H. Elbiaze, “Fair Congestion Control Protocol for Data Center Bridging,” IEEE Systems Journal, vol. 13, no. 4, pp. 4134–4145, 2019.

I. Cho, K. Jang, and D. Han, “Credit-Scheduled Delay-bounded Congestion Control for Datacenters,” in SIGCOMM 2017 - Proceedings of the 2017 Conference of the ACM Special Interest Group on Data Communication, 2017, pp. 239–252.

M. R. Kanagarathinam, S. Singh, I. Sandeep, A. Roy, and N. Saxena, “DTCP: Dynamic TCP Congestion Control Algorithm for Next Generation Mobile Networks,” in 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC), 2018, pp. 1–6.

T. M. Mitchell, Machine Learning, ser. McGraw-Hill International Editions. McGraw-Hill, 1997.

Y. Freund and R. E. Schapire, “A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting,” Journal of Computer and System Sciences, vol. 55, no. 1, pp. 119–139, 1997. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S002200009791504X

C. D. Maciel and C. M. Ritter, “TCP/IP Networking in Process Control Plants,” Computers & Industrial Engineering, vol. 35, no. 3, pp. 611–614, 1998. [Online]. Available: https://doi.org/10.1016/S0360-8352(98)00171-5

C. Caini and R. Firrincieli, “TCP Hybla: A TCP Enhancement for Heterogeneous Networks,” International Journal of Satellite Communications and Networking, vol. 22, no. 5, pp. 547–566, 2004.

A. Mozo, J. L. López-Presa, and A. Fernández Anta, “A distributed and Quiescent Max-Min Fair Algorithm for Network Congestion Control,” Expert Systems with Applications, vol. 91, pp. 492–512, 2018.

J. R. Carrión Torres, “Aplicabilidad de Funciones de Utilidad Para el Control de Congestión en Redes de Computadores,” Master’s thesis, Universidad Técnica Particular de Loja, Loja, 2020.

R. Al-Saadi, G. Armitage, J. But, and P. Branch, “A Survey of Delay-Based and Hybrid TCP Congestion Control Algorithms,” IEEE Communications Surveys and Tutorials, vol. 21, no. 4, pp. 3609–3638, 2019.

M. Welzl, Network Congestion Control: Managing Internet Traffic. J. Wiley, 2005.

J. Jin, W.-H. Wang, and M. Palaniswami, “Utility Max–Min Fair Resource Allocation for Communication Networks with Multipath Routing,” Computer Communications, vol. 32, no. 17, pp. 1802–1809, 2009. [Online]. Available: https://doi.org/10.1016/j.comcom.2009.06.014

L. Chen, B. Wang, X. Chen, X. Zhang, and D. Yang, Utility-Based Resource Allocation for Mixed Traffic in Wireless Networks. Institute of Electrical and Electronics Engineers, 2011.

S. Li, Y. Zhang, Y. Wang, and W. Sun, “Utility Optimization-Based Bandwidth Allocation for Elastic and Inelastic Services in Peer-to-Peer Networks,” International Journal of Applied Mathematics and Computer Science, vol. 29, no. 1, pp. 111–123, 2019.

Q. V. Pham and W. J. Hwang, “Network Utility Maximization-Based Congestion Control over Wireless Networks: A Survey and Potential Directives,” IEEE Communications Surveys and Tutorials, vol. 19, no. 2, pp. 1173–1200, 2017.

M. Chiang, “Distributed Network Control Through Sum Product Algorithm on Graphs,” in Global Telecommunications Conference, 2002. GLOBECOM ’02. IEEE, vol. 3, 2002, pp. 2395–2399 vol.3.

A. S. Tanenbaum and D. J. Wetherall, Computer Networks, 5th ed. Prentice Hall, 2011.

C. Demichelis and P. Chimento. (2002) IP Packet Delay Variation Metric for IP Performance Metrics (IPPM). Network Working Group, RFC 3393.

C. Gini, Variabilità e mutabilità, 1912, vol. 5, no. 20.

M. O. Lorenz, “Methods of Measuring the Concentration of Wealth,” Publications of the American Statistical Association, vol. 9, no. 70, pp. 209–219, 1905.

G. F. Riley and T. R. Henderson, “The NS-3 Network Simulator.” in Modeling and Tools for Network Simulation, K. Wehrle, M. Günes, and J. Gross, Eds. Springer, 2010, pp. 15–34. [Online]. Available: http://dblp.uni-trier.de/db/books/collections/Wehrle2010.html#RileyH10



How to Cite

Segarra Guzmán, E., & Ludeña-González, P. (2024). Distributed Congestion Control Based on Utility Function. Enfoque UTE, 15(2), 9-19. https://doi.org/10.29019/enfoqueute.994