In active queue management (AQM), core routers signal transmission control protocol (TCP) sources with the objective of managing queue utilization and delay. It is essentially a feedback control problem. Based on a recently developed dynamic model of TCP congestion-avoidance mode, this paper does three things: 1) it relates key network parameters such as the number of TCP sessions, link capacity and round-trip time to the underlying feedback control problem; 2) it analyzes the present de facto AQM standard: random early detection (RED) and determines that REDs queue-averaging is not beneficial; and 3) it recommends alternative AQM schemes which amount to classical proportional and proportional-integral control. We illustrate our results using ns simulations and demonstrate the practical impact of proportional-integral control on managing queue utilization and delay.

/pdf/analysis-and-design-of-controllers-for-aqm-routers-39jw7xy9be.pdf

Analysis and design of controllers for AQM routers supporting TCP flows

Since its formal introduction to IP networks in 1993 as a viable complementary approach for congestion control, there has been a steady stream of research output with respect to Active Queue Management (AQM). This survey attempts to travel the trajectory of AQM research from 1993 with the first algorithm, Random Early Detection (RED), to current work in 2011. In this survey we discuss the general attributes of AQM schemes, and the design approaches taken such as heuristic, control-theoretic and deterministic optimization. Of interest is the role of AQM in QoS provisioning particularly in the DiffServ context, as well as the role of AQM in the wireless domain. For each section, example algorithms from the research literature are presented.

Active Queue Management: A Survey

Foreword. Preface. List of Tables. List of Figures. 1. Introduction . 1.1 Who should read this book? 1.2 Contents. 1.3 Structure. 2. Congestion control principles. 2.1 What is congestion? 2.2 Congestion collapse. 2.3 Controlling congestion: design considerations. 2.4 Implicit feedback. 2.5 Source behaviour with binary feedback. 2.6 Stability. 2.7 Rate-based versus window-based control. 2.8 RTT estimation. 2.9 Traffic phase effects. 2.10 Queue management. 2.11 Scalability. 2.12 Explicit feedback. 2.13 Special environments. 2.14 Congestion control and OSI layers. 2.15 Multicast congestion control. 2.16 Incentive issues. 2.17 Fairness. 2.18 Conclusion. 3. Present technology. 3.1 Introducing TCP. 3.2 TCP window management. 3.3 TCP RTO calculation. 3.4 TCP congestion control and reliability. 3.5 Concluding remarks about TCP. 3.6 The Stream Control Transmission Protocol (SCTP). 3.7 Random Early Detection (RED). 3.8 The ATM'Available Bit Rate' service. 4. Experimental enhancements. 4.1 Ensuring appropriate TCP behaviour. 4.2 Maintaining congestion state. 4.3 Transparent TCP improvements. 4.4 Enhancing active queue management. 4.5 Congestion control for multimedia applications. 4.6 Better-than-TCP congestion control. 4.7 Congestion control in special environments. 5. Internet traffic management - the ISP perspective. 5.1 The nature of Internet traffic. 5.2 Traffic engineering. 5.3 Quality of Service (QoS). 5.4 Putting it all together. 6. The future of Internet congestion control. 6.1 Small deltas or big ideas? 6.2 Incentive issues. 6.3 Tailor-made congestion control. Appendix A: Teaching congestion control with tools. A.1 CAVT. A.1.1 Writing script. A.1.2 Teaching with CAVT. A.1.3 Internals. A.2 ns. A.2.1 Using ns for teaching: the problem. A.2.2 Using ns for teaching: the solution. A.2.3 NSBM. A.2.4 Example exercises. Appendix B: Related IETF work. B.1 Overview. B.2 Working groups. B.3 Finding relevant documents. Appendix C: List of abbreviations. Bibliography. Index.

https://leseprobe.buch.de/images-adb/8b/9f/8b9f6edb-563b-46aa-a7a1-014b5cc86f60.pdf

Network Congestion Control: Managing Internet Traffic

http://home.eps.hw.ac.uk/~cw46/2010_XiongN_InfoScience_10_11_2249.pdf

A novel self-tuning feedback controller for active queue management supporting TCP flows

http://alpha.math.uga.edu/~mjlai/papers/LaiStnew.pdf

Construction of multivariate compactly supported tight wavelet frames

Active queue management (AQM) is an effective method to enhance congestion control, and to achieve tradeoff between link utilization and delay. The de facto standard, random early detection (RED), and most of its variants use queue length as a congestion indicator to trigger packet dropping. The proportional-integral (PI), use both queue length and traffic input rate as congestion indicators; effective stability model and practical design rules built on the TCP control model and abstracted AQM model reveal that such schemes enhance the stability of a system. In this paper, we propose an AQM scheme with fast response time, yet good robustness. The scheme, called loss ratio based RED (LRED), measures the latest packet loss ratio, and uses it as a complement to queue length in order to dynamically adjust packet drop probability. Employing the closed-form relationship between packet loss ratio and the number of TCP flows, this scheme is responsive even if the number of TCP flows varies significantly. We also provide the design rules for this scheme based on the well-known TCP control model. This scheme's performance is examined under various network configurations, and compared to existing AQM schemes, including PI, random exponentially marking (REM), and adaptive virtual queue (AVQ). Our simulation results show that, with comparable complexity', this scheme has short response time, better robustness, and more desirable tradeoff than PI, REM, and AQV, especially under highly dynamic network and heavy traffic load.

/pdf/lred-a-robust-active-queue-management-scheme-based-on-packet-1cn0suuapd.pdf

LRED: a robust active queue management scheme based on packet loss ratio

This paper proposes an adaptive proportional integral (API) algorithm for active queue management. API uses mean packet loss ratio and mean queue length as control error to adaptively adjust packet drop probability in order to expedite congestion control. The extensive simulations validate that API achieves faster control response and improved performance in terms of goodput, average queue length, and packet loss ratio.

An adaptive algorithm for active queue management

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LRED: a robust active queue management scheme based on packet loss ratio

An adaptive algorithm for active queue management