Flow control (data)

Abstract: Congestion avoidance mechanisms allow a network to operate in the optimal region of low delay and high throughput, thereby, preventing the network from becoming congested. This is different from the traditional congestion control mechanisms that allow the network to recover from the congested state of high delay and low throughput. Both con- gestion avoidance and congestion control mechanisms are basi- cally resource management problems. They can be formulated as system control problems in which the system senses its state and feeds this back to its users who adjust their controls. The key component of any congestion avoidance scheme is the algorithm (or control function) used by the users to in- crease or decrease their load (window or rate). We abstractly characterize a wide class of such increase/decreas e algorithms and compare them using several different performance metrics. They key metrics are efficiency, fairness, convergence time, and size of oscillations. It is shown that a simple additive increase and multiplicative decrease algorithm satisfies the sufficient conditions for con- vergence to an efficient and fair state regardless of the starting state of the network. This is the algorithm finally chosen for implementation in the congestion avoidance scheme recom- mended for Digital Networking Architecture and OSI Trans- port Class 4 Networks.

Abstract: This paper considers the potentially negative impacts of an increasing deployment of non-congestion-controlled best-effort traffic on the Internet. These negative impacts range from extreme unfairness against competing TCP traffic to the potential for congestion collapse. To promote the inclusion of end-to-end congestion control in the design of future protocols using best-effort traffic, we argue that router mechanisms are needed to identify and restrict the bandwidth of selected high-bandwidth best-effort flows in times of congestion. The paper discusses several general approaches for identifying those flows suitable for bandwidth regulation. These approaches are to identify a high-bandwidth flow in times of congestion as unresponsive, "not TCP-friendly", or simply using disproportionate bandwidth. A flow that is not "TCP-friendly" is one whose long-term arrival rate exceeds that of any conformant TCP in the same circumstances. An unresponsive flow is one failing to reduce its offered load at a router in response to an increased packet drop rate, and a disproportionate-bandwidth flow is one that uses considerably more bandwidth than other flows in a time of congestion.

Abstract: This article reviews the current transmission control protocol (TCP) congestion control protocols and overviews recent advances that have brought analytical tools to this problem. We describe an optimization-based framework that provides an interpretation of various flow control mechanisms, in particular, the utility being optimized by the protocol's equilibrium structure. We also look at the dynamics of TCP and employ linear models to exhibit stability limitations in the predominant TCP versions, despite certain built-in compensations for delay. Finally, we present a new protocol that overcomes these limitations and provides stability in a way that is scalable to arbitrary networks, link capacities, and delays.

Abstract: From the Publisher: Flow control and optimization has been an important part of experimental flow science throughout the last century. As research in computational fluid dynamics (CFD) matured, CFD codes were routinely used for the simulation of fluid flows. Subsequently, mathematicians and engineers began examining the use of CFD algorithms and codes for optimization and control problems for fluid flows. The marriage of mature CFD methodologies with state-of-the-art optimization methods has become the center of activity in computational flow control and optimization. Perspectives in Flow Control and Optimization presents flow control and optimization as a subdiscipline of computational mathematics and computational engineering. It introduces the development and analysis of several approaches for solving flow control and optimization problems through the use of modern CFD and optimization methods. The author discusses many of the issues that arise in the practical implementation of algorithms for flow control and optimization, such as choices to be made and difficulties to overcome. He provides the reader with a clear idea of what types of flow control and optimization problems can be solved, how to develop effective algorithms for solving such problems, and potential problems to be aware of when implementing the algorithms. This book is written for both those new to the field of control and optimization as well as experienced practitioners, including engineers, applied mathematicians, and scientists interested in computational methods for flow control and optimization. Both those interested in developing new algorithms and those interested in the application of existing algorithms should find useful information in this book. Readers with a solid background in calculus and only slight familiarity with partial differential equations should find the book easy to understand. Knowledge of fluid mechanics, computational fluid dynamics, calculus of variations, control theory or optimization is beneficial, but is not essential, to comprehend the bulk of the presentation. Only Chapter 6 requires a substantially higher level of mathematical knowledge, most notably in the areas of functional analysis, numerical analysis, and partial differential equations. Fortunately, this chapter is completely independent of the others so that, even if this chapter is not well understood, the majority of the book should still prove useful and informative. About the Author Max D. Gunzburger is a Distinguished Professor in the Department of Mathematics at Iowa State University and also Francis Eppes Professor in the School for Computational Science and Information Technology and the Department of Mathematics at Florida State University. An active member of both AMS and SIAM, he is Editor-in-Chief of the SIAM Journal on Numerical Analysis and Associate Editor of the SIAM Journal on Control and Optimization. He also serves on the editorial boards of the SIAM Advances in Design and Control book series and the International Journal for Computational Fluid Dynamics.