Congestion Control for Layered Multicast Transmission

peer reviewedHeterogeneity of receivers makes it hard to control congestion for multicast transmission. Using hierarchical layering of the information is one of the most elegant and efficient approach to tackle this problem. The proposed algorithm is based on this principle and has three objectives: to fulfill intra-session fairness, i.e. between different receivers of the same session; to be fair towards TCP; to fulfill inter-session fairness, i.e. same throughputs (and not number of layers) to concurrent sessions

Contrôle de congestion pour la transmission multipoint en couches

Le contrôle de congestion en transmission multipoints est rendu difficile par l'hétérogénéité des récepteurs. En effet, pour la transmission vidéo par exemple, il serait peu raisonnable que l'émetteur adapte son débit en fonction du récepteur le moins performant ou de celui qui subit temporairement la congestion la plus sévère. Pour contourner ce problème, l'émetteur peut émettre un flux structuré en couches, de telle sorte que la couche de base donne une qualité minimale et que les couches suivantes améliorent successivement cette qualité. L'algorithme de contrôle de congestion proposé est basé sur ce schéma. Il permet à chaque récepteur de sélectionner dynamiquement un sous-ensemble adéquat de couches en répondant aux objectifs suivants. Premièrement, l'algorithme doit être équitable vis-à-vis de TCP; ce qui signifie que les débits du flux multicouches reçu et celui d'un flux TCP placé dans les mêmes conditions doivent être plus ou moins les mêmes. Deuxièmement, les récepteurs doivent être suffisamment coordonnés pour qu'une congestion résultant de l'ajout d'une couche par l'un d'eux ne puisse être interprétée par un autre récepteur comme une congestion résultant de ses propres décisions ou d'un trafic perturbateur. Enfin, lorsque deux sessions multicouches empruntent un même goulet, nous souhaitons que les récepteurs obtiennent le même débit, ce qui signifiera en général des nombres de couches différents si les débits des couches sont distincts

A Stable and Flexible TCP-friendly congestion control protocol for layered multicast transmission

peer reviewedWe propose an improvement of our RLS (Receiver-driven Layered multicast with Synchronization points) protocol, called CIFL for “Coding-Independent Fair Layered mulaticast”, along two axes. In CIFL, each receiver of a layered multicast transmission will try and find the adequate number of layers to subscribe to, so that the associated throughput is fair towards TCP and stable in steady-state. The first improvement is that CIFL is not specific to any coding scheme. It can work as well with an exponentially distributed set of layers (where the throughput of each layer i equals the sum of the throughputs of all layers below i), or with layers of equal throughputs, or any other scheme. The second improvement is the excellent stability of the protocol which avoids useless join attempts by learning from its unsuccessful previous attempts in the same (or better) network conditions. Moreover, the protocol tries and reaches its ideal TCP-friendly as soon as possible by computing its target throughput in a clever way when an incipient congestion is confirmed

A machine learning approach to improve congestion control over wireless computer networks

In this paper, we present the application of machine learning techniques to the improvement of the congestion control of TCP in wired/wireless networks. TCP is suboptimal in hybrid wired/wireless networks because it reacts in the same way to losses due to congestion and losses due to link errors. We thus propose to use machine learning techniques to build automatically a loss classifier from a database obtained by simulations of random network topologies. For this classifier to be useful in this application, it should satisfy both a computational constraint and a time varying constraint on its misclassification rate on congestion losses. Several machine learning algorithms are compared with these two constraints in mind. The best method for this application appears to be decision tree boosting. It outperforms ad hoc classifiers proposed in the networking literature and its combination with TCP improves significantly the bandwidth usage over wireless networks and does not deteriorate the good behaviour of TCP over wired networks. This study thus shows the interest of the application of machine learning techniques for the design of protocol in computer networks.

On the Accuracy of Analytical Models of TCP Throughput

Abstract. Based on a large set of TCP sessions we first study the accuracy of two well-known analytical models (SQRT and PFTK) of the TCP average rate. This study shows that these models are far from being accurate on average. Actually, our simulations show that 70 % of their predictions exceed the boundaries of TCP-Friendliness, thus questioning their use in the design of new TCP-Friendly transport protocols. Our study also shows that the inaccuracy of the PFTK model is largely due to its inability to make the distinction between the two packet loss detection methods used by TCP: triple duplicate acknowledgments or timeout expirations. We then use supervised learning techniques to infer models of the TCP rate. These models show important accuracy improvements when they take into account the two types of losses. This suggests that analytical model of TCP throughput should certainly benefit from the incorporation of the timeout loss rate.

Enhancement of TCP over wired/wireless networks with packet loss classifiers inferred by supervised learning

TCP is suboptimal in heterogeneous wired/wireless networks because it reacts in the same way to losses due to congestion and losses due to link errors. In this paper, we propose to improve TCP performance in wired/wireless networks by endowing it with a classifier that can distinguish packet loss causes. In contrast to other proposals we do not change TCP’s congestion control nor TCP’s error recovery. A packet loss whose cause is classified as link error will simply be ignored by TCP’s congestion control and recovered as usual, while a packet loss classified as congestion loss will trigger both mechanisms as usual. To build our classification algorithm, a database of pre-classified losses is gathered by simulating a large set of random network conditions, and classification models are automatically built from this database by using supervised learning methods. Several learning algorithms are compared for this task. Our simulations of different scenarios show that adding such a classifier to TCP can improve the throughput of TCP substantially in wired/wireless networks without compromizing TCP-friendliness in both wired and wireless environments