A New Estimation Scheme for the Effective Number of Users in Internet Congestion Control

Many congestion control protocols have been recently proposed in order to alleviate the problems encountered by TCP in high-speed networks and wireless links. Protocols utilizing an architecture that is in the same spirit as the ABR service in ATM networks require estimates of the effective number of users utilizing each link in the network to maintain stability in the presence of delays. In this paper, we propose a novel estimation algorithm that is based on online parameter identification techniques and is shown through analysis and simulations to converge to the effective number of users utilizing each link. The algorithm does not require maintenance of per-flow states within the network or additional fields in the packet header, and it is shown to outperform previous proposals that were based on pointwise division in time. The estimation scheme is designed independently from the control functions of the protocols and is thus universal in the sense that it operates effectively in a number of congestion control protocols. It can thus be successfully used in the design of new congestion control protocols. In this paper, to illustrate its universality, we use the proposed estimation scheme to design a representative set of Internet congestion control protocols. Using simulations, we demonstrate that these protocols satisfy key design requirements. They guide the network to a stable equilibrium that is characterized by high network utilization, small queue sizes, and max-min fairness. In addition, they are scalable with respect to changing bandwidths, delays, and number of users, and they generate smooth responses that converge quickly to the desired equilibrium.

[1]  Sungho Kang,et al.  A simple, scalable, and stable explicit rate allocation algorithm for MAX-MIN flow control with minimum rate guarantee , 2001, TNET.

[2]  Nick McKeown,et al.  Processor Sharing Flows in the Internet , 2005, IWQoS.

[3]  Lachlan L. H. Andrew,et al.  MaxNet : Theory and Implementation , 2006 .

[4]  Anurag Kumar,et al.  Performance of TCP congestion control with explicit rate feedback , 2005, IEEE/ACM Transactions on Networking.

[5]  Jean C. Walrand,et al.  Explicit rate flow control for ABR services in ATM networks , 2000, TNET.

[6]  Anuradha M. Annaswamy,et al.  Robust Adaptive Control , 1984, 1984 American Control Conference.

[7]  Semyon M. Meerkov,et al.  Feedback control of congestion in packet switching networks: the case of a single congested node , 1993, TNET.

[8]  James Aweya,et al.  A simple, scalable and provably stable explicit rate computation scheme for flow control in communication networks , 2001, Int. J. Commun. Syst..

[9]  E. Altman,et al.  Multi-user rate-based flow control with action delays: a team-theoretic approach , 1997, Proceedings of the 36th IEEE Conference on Decision and Control.

[10]  F. Bonomi,et al.  A novel explicit rate congestion control algorithm , 1998, IEEE GLOBECOM 1998 (Cat. NO. 98CH36250).

[11]  Sally Floyd,et al.  HighSpeed TCP for Large Congestion Windows , 2003, RFC.

[12]  Ren Wang,et al.  TCP westwood: Bandwidth estimation for enhanced transport over wireless links , 2001, MobiCom '01.

[13]  Sanjay Shakkottai,et al.  TCP performance over end-to-end rate control and stochastic available capacity , 2001, TNET.

[14]  Dmitri Loguinov,et al.  JetMax: Scalable Max-Min Congestion Control for High-Speed Heterogeneous Networks , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[15]  T. V. Lakshman,et al.  The performance of TCP/IP for networks with high bandwidth-delay products and random loss , 1997, TNET.

[16]  Cheng Jin,et al.  FAST TCP: Motivation, Architecture, Algorithms, Performance , 2006, IEEE/ACM Transactions on Networking.

[17]  San-qi Li,et al.  A linear dynamic model for design of stable explicit-rate ABR control schemes , 1997, Proceedings of INFOCOM '97.

[18]  San-qi Li,et al.  An ABR feedback control scheme with tracking , 1997, Proceedings of INFOCOM '97.

[19]  Larry L. Peterson,et al.  TCP Vegas: End to End Congestion Avoidance on a Global Internet , 1995, IEEE J. Sel. Areas Commun..

[20]  Karl Johan Åström,et al.  Adaptive Control , 1989, Embedded Digital Control with Microcontrollers.

[21]  K. K. Ramakrishnan,et al.  Time scale analysis scalability issues for explicit rate allocation in ATM networks , 1996, TNET.

[22]  Mark Handley,et al.  Congestion control for high bandwidth-delay product networks , 2002, SIGCOMM.

[23]  Sally Floyd,et al.  Connections with multiple congested gateways in packet-switched networks part 1: one-way traffic , 1991, CCRV.

[24]  Michael K. Wong,et al.  Novel explicit rate congestion control algorithm , 1998, Other Conferences.

[25]  Saverio Mascolo,et al.  The effect of reverse traffic on the performance of new TCP congestion control algorithms , 2022 .

[26]  Randall Berry,et al.  A linear control approach to explicit rate feedback in ATM networks , 1997, Proceedings of INFOCOM '97.

[27]  Shivkumar Kalyanaraman,et al.  TCP rate control , 2000, CCRV.

[28]  T. Basar,et al.  A distributed globally convergent algorithm for fair, queue-length-based congestion control , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).

[29]  Fernando Paganini,et al.  Dynamics of TCP/RED and a scalable control , 2002, Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies.

[30]  Cheng Jin,et al.  FAST TCP: Motivation, Architecture, Algorithms, and Performance , 2004, INFOCOM.

[31]  K. K. Ramakrishnan,et al.  An efficient rate allocation algorithm for ATM networks providing max-min fairness , 1995, HPN.

[32]  Andreas Pitsillides,et al.  Adaptive congestion protocol: A congestion control protocol with learning capability , 2007, Comput. Networks.

[33]  Saverio Mascolo,et al.  Congestion control in high-speed communication networks using the Smith principle , 1999, Autom..