Internet congestion control using the power metric: Keep the pipe just full, but no fuller

Abstract Recently there has been considerable interest in a key paper [1] describing a new approach to congestion control in Internet traffic which has resulted in significant network performance improvement. The approach is based on a 1978 paper [2] and a companion 1979 paper [3] which identified a system operating point that was optimal in that it maximized delivered throughput while minimizing delay and loss. This operating point is simply characterized by the insight that one should “Keep the pipe just full, but no fuller” and we show this is equivalent to loading the system so that in many cases (including those relevant to TCP connections) the optimized average number in the pipe is exactly equal to the Bandwidth-Delay Product. It is important to understand the reasoning and intuition behind this early insight and why it provides such improved behavior of systems and networks. In this paper, we first develop this insight using purely deterministic reasoning. We then extend this reasoning by examining far more complex stochastic queueing systems and networks using a function called Power to mathematically and graphically extract exact and surprising results that support the insight and allow us to identify the optimum operating point for a broad class of systems. These observations allow us to study the impact of Power on networks leading eventually to supporting the statements about steady state congestion and flow control as presented in [1] for today’s Internet. We point out that the discussions about the latest congestion control algorithms [ 1 , 4, 5, 6, 7, 8, 9, 10, 11] address the dynamics of tracking flow, dealing with multiple intersecting flows, fairness, and more, and which focus on the dynamic behavior of data networks whereas our work here focuses only on the steady state behavior.

[1]  S. Wittevrongel,et al.  Queueing Systems , 2019, Introduction to Stochastic Processes and Simulation.

[2]  J. R. Jackson Networks of Waiting Lines , 1957 .

[3]  Leonard Kleinrock,et al.  Power and deterministic rules of thumb for probabilistic problems in computer communications , 1979 .

[4]  Yechiam Yemini,et al.  A balance of power principle for decentralized resource sharing , 2014, Comput. Networks.

[5]  Leonard Kleinrock,et al.  Communication Nets: Stochastic Message Flow and Delay , 1964 .

[6]  B. Avi-Itzhak,et al.  A SEQUENCE OF SERVICE STATIONS WITH ARBITRARY INPUT AND REGULAR SERVICE TIMES , 1965 .

[7]  Van Jacobson,et al.  Congestion avoidance and control , 1988, SIGCOMM '88.

[8]  Martina Zitterbart,et al.  Experimental evaluation of BBR congestion control , 2017, 2017 IEEE 25th International Conference on Network Protocols (ICNP).

[9]  Alexander Afanasyev,et al.  Host-to-Host Congestion Control for TCP , 2010, IEEE Communications Surveys & Tutorials.

[10]  Luigi Fratta,et al.  The flow deviation method: An approach to store-and-forward communication network design , 1973, Networks.

[12]  Yuchung Cheng,et al.  BBR , 2017, Commun. ACM.

[13]  Gerardo Rubino On Kleinrock's Power Metric for Queueing Systems , 2011, 2011 Proceedings of 20th International Conference on Computer Communications and Networks (ICCCN).

[14]  V. Jacobson,et al.  Congestion avoidance and control , 1988, CCRV.

[15]  Jr. Harry Richard Gail On the optimization of computer network power , 1983 .

[16]  Alfred Giessler,et al.  Free Buffer Allocation - An Investigation by Simulation , 1978, Comput. Networks.

[17]  Jeffrey M. Jaffe,et al.  Flow Control Power is Nondecentralizable , 1981, IEEE Trans. Commun..

[18]  Leonard Kleinrock,et al.  Certain analytic results for time-shared processors , 1968, IFIP Congress.

[19]  Leonard Kleinrock,et al.  Performance of Distributed Multi-Access Computer-Communication Systems , 1977, IFIP Congress.

[20]  Injong Rhee,et al.  CUBIC: a new TCP-friendly high-speed TCP variant , 2008, OPSR.

[21]  R. Stephenson A and V , 1962, The British journal of ophthalmology.

[22]  Izhak Rubin Communication networks: Message path delays , 1974, IEEE Trans. Inf. Theory.

[23]  Martina Zitterbart,et al.  TCP LoLa: Congestion Control for Low Latencies and High Throughput , 2017, 2017 IEEE 42nd Conference on Local Computer Networks (LCN).