K. Kar, S. Sarkar, L. Tassiulas, Optimization based rate control for multirate multicast sessions, in: Proceedings of IEEE INFOCOM, April 2001.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....marking strategy. Similarly, in [20] an algorithm has been proposed for computing maxmin fair rates in a multicast network. In [21] the authors have extended the algorithm for the computation of max min fair rates when discrete bandwidth layers are available as in the case of layered video. In [6], an optimization based rate control mechanism, based on a utility maximizing framework, has been proposed for multirate multicast sessions, which tries to solve the dual of a convex program formulation of the problem. Even though this takes into account the heterogeneous requirements of the ....

K. Kar, S. Sarkar, and L. Tassiulas. Optimization based rate control for multirate multicast sessions. In Proceedings of the IEEE INFOCOM, 2001.

....scaling yields useful expressions that can be used to help dimension networks and identify bottlenecks. Multi class admission control achieves the QoS benefits of optimal adaptation but requires accurate knowledge of system parameters. Related work includes [10] 7] 11] 12] 13] [14], 15] 8] 10] investigates optimal policies to dynamically adapt the fraction of the available bandwidth given to a base and enhancement layer. Their work differs from ours in that it takes is a client centric view while ours is a system centric view. Both [7] and [8] use an almost identical ....

....issues, but focuses on services for clients with heterogeneous access line rates. They focus on aligning offered subscription levels to the bandwidth available to clients in this environment and therefore come to different conclusions regarding the benefit of providing additional encoding levels. [14] offers a system level analysis of rate adaptive streams, but in a static context, i.e. a fixed number of streams. 15] investigates a model where the server dynamically adjusts the number and rate of each subscription layer in response to congestion feedback. We feel such server adaptive models ....

Koushik Kar, Saswati Sarkar, and Leandros Tassiulas, "Optimization Based Rate Control For Multirate Multicast Sessions," Tech. Rep., Institute of Systems Research and University of Maryland, 2000.

....Control Algorithm for Multi rate Multicast Flows The paper ID number: 325 The total number of pages: 14 ABSTRACT We present a distributed algorithm to compute bandwidth max min fair rates in a multi rate multicast network. The significance of the algorithm, compared to previous algorithms [14, 8, 13], is that it is scalable in that it does not require each link to maintain the saturation status of all sessions and VSs travelling through it, stable in that it converges asymptotically to the desired equilibrium satisfying the minimum plus max min fairness, and has explicit link bu#er control in ....

....to proof of network wide inter session fairness and algorithm stability, and, more importantly, those schemes are inevitably subject to coarse grained layering. On the other hand, multi rate multicast flow control algorithms focusing on fair rate allocation have been proposed and analyzed [14, 8, 13]. These algorithms di#er in intersession fairness achievable, respectively adopting bandwidth max min fairness, utility maximal fairness, and aggregate utility maximization fairness as the target fairness. The disadvantage of these algorithm as compared to RLM and its variants is di#culty in the ....

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K. Kar, S. Sakar, and L. Tassiulas. Optimization based rate control for multirate multicast. In Proc. of IEEE INFOCOM '01, pages 123--132. IEEE, 2001.

.... TCP congestion control algorithm can be interpreted as carrying out a distributed primal dual algorithm over the Internet to maximize aggregate utility, and a user s utility function is (often implicitly) defined by its TCP algorithm, see e.g. 8] 12] 15] 16] 13] 11] 9] for unicast and [7], 3] for multicast. All of these papers assume that routing is given and fixed at the time scale of interest, and TCP, together with active queue management (AQM) attempt to maximize aggregate utility over source rates. In this paper, we study utility maximization at the time scale of route ....

Koushik Kar, Saswati Sarkar, and Leandros Tassiulas. Optimization based rate control for multirate multicast sessions. In Proceedings of IEEE Infocom, April 2001.

....It employs an absolute utility function that depends only on the received bandwidth. Optimal algorithm using relative utility functions are presented in [6,11] These allocation algorithms use end toend adaptation for the Internet environment and focus only on a single session case. Kar et al. [8] presented a distributed algorithm that maximizes the total utility for all the receivers belonging to different sessions by employing some intermediaries. In the above optimization schemes, the number of layers is usually assumed to be predetermined. Layering overheads, in particular, the ....

K. Kar, S. Sarkar, and L. Tassiulas, "Optimization based rate control for multirate multicast sessions," in Proceedings of IEEE INFOCOM'01, April 2001.

....data as multiple layers, with each layer being sent to a separate multicast group address. Receivers measure congestion and determine the number of groups they join. These schemes could be further classi ed as end to end (eg: RLM, RLC, and FLID DL [16, 24, 5, 7, 3] or network feedback driven (eg: [14, 22]) In contrast to our work, the network feedback driven schemes aim to achieve max min fairness, but require complex support at all nodes in the multicast tree. The end to end receiver based approach is attractive because of its scalability to very large group sizes. But recent analysis by ....

Kar K., Sarkar S. and Tassiulas L., \Optimization Based Rate Control for Multirate Multicast Sessions," INFOCOM '01, Apr '01.

....layers, with each layer being sent to a separate multicast group address. Receivers measure congestion and determine the number of groups they join. These schemes could be further classified as end to end (eg: RLM, RLC, and FLID DL [15] 26] 6] 8] 4] or network feedback driven (eg: [13], 20] II. LE SBCC: SCHEME DESCRIPTION The LE SBCC scheme consists of a purely sourcebased cascaded set of filters and RTT estimation modules feeding into a rate adaptation module (which could be additive increase multiplicative decrease (AIMD) or TFRC[9] Figure 1) We use AIMD in a ....

Kar K., Sarkar S. and Tassiulas L., Optimization Based Rate Control for Multirate Multicast Sessions, INFOCOM

....[30] and [22] The problem is solved using a duality approach in [25] leading to an abstract algorithm whose convergence has been proved in asynchronous environment. A practical implementation of this algorithm is proposed in [4] The duality approach is extended to multirate multicast setting in [17]. The idea of treating source rates as primal variables and congestion measures (queueing delay in Vegas) as dual variables, and TCP AQM as a distributed primal dual algorithm to solve (4 5) with different utility functions, is extended in [24, 26] to other schemes, such as Reno DropTail, ....

Koushik Kar, Saswati Sarkar, and Leandros Tassiulas. Optimization based rate control for multirate multicast sessions. In Proceedings of IEEE Infocom, April 2001.

....considerably more complex than its unicast version. For instance, the problem in the multirate multicast case is non separable and non differentiable, unlike the unicast case (we discuss more on this in the subsequent sections) The multirate multicast utility maximization problem is addressed in [11]. Here, the authors propose distributed algorithms for this problem; their approach is based on dual methods. In this paper, we take a different approach, and derive a primal algorithm based on non differentiable optimization methods. The algorithm that we propose is distributed, scalable, and ....

....and the overhead of the communication between the network and the user is very low. Moreover, in our algorithm, per session states need not be maintained at the network links. These features make the algorithm attractive in terms of practical deployment. On the other hand, the algorithms in [11] suffer from several practical shortcomings (they have high overhead of computation and communication, and require the network links to maintain persession state) A detailed comparison of the algorithm proposed in this paper and those in [11] is presented in Section VIII of this paper. It is ....

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K. Kar, S. Sarkar, L. Tassiulas, "Optimization Based Rate Control for Multirate Multicast Sessions", Proceedings of Infocom 2001.

....more complex than its unicast version. 1 For instance, the problem in the multirate multicast case is non separable and non differentiable, unlike the unicast case (we discuss more on this in the subsequent sections) The multirate multicast utility maximization problem is addressed in [10]. Here, the authors propose distributed algorithms for this problem; their approach is based on dual methods. In this paper, we take a different approach, and derive a primal algorithm based on non differentiable optimization methods. The algorithm that we propose is distributed, scalable, and ....

....and the overhead of the communication between the network and the user is very low. Moreover, in our algorithm, persession states need not be maintained at the network links. These features make the algorithm very attractive in terms of practical deployment. On the other hand, the algorithms in [10] suffer from several practical shortcomings (they have high overhead of computation and communication, and require the network links to maintain per session state) A detailed comparison of the algorithm proposed in this paper and those in [10] is presented in Section IX of this paper. The paper ....

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K. Kar, S. Sarkar, L. Tassiulas, "Optimization Based Rate Control for Multirate Multicast Sessions", Proceedings of Infocom

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K. Kar, S. Sarkar, L. Tassiulas, "Optimization Based Rate Control for Multirate Multicast Sessions", Proceedings of Infocom

....multicast networks in [9] An alternate notion of fairness is to allocate bandwidth so as to maximize the sum of the total user utilities. Recently, 5,7,8] proposed distributed algorithms for computing the bandwidth required to maximize the sum of the user utilities in unicast networks and [6] addressed this problem in context of multirate, multicast networks. Our objective in this paper is to provide scheduling policy for attaining maxmin fair utilities in networks. This problem has not been addressed before in unicast or multicast networks. In this paper we will address the unicast ....

K. Kar, S. Sarkar, L. Tassiulas. \Optimization Based Rate Control for Multirate Multicast Sessions," Proceedings of INFOCOM'

....j 2 J) in an optimal solution may not be unique, though. Theorem 1: Let y = y j ; j 2 J) be any optimal solution of P 0 . Then y J = y j ; j 2 J) is the unique optimal solution of P. The proof of the above result is straightforward, and is left to the reader (it can also be found in [5]) The theorem shows that we can obtain the optimum solution of P by solving P 0 . Note that the variables (y j ; j 2 J) may not necessarily be equal to the corresponding max values (the actual rates on the corresponding branches) at optimality, but may be greater, as (4) indicates. We will ....

....the following convergence result. Theorem 2: Consider dual subgradient algorithm as described in (21) 24) with the step sizes satisfying (20) Then the sequence of vectors y (n) J = y (n) j ; j 2 J) converges to the unique optimal solution of P. The proof of the above theorem is stated in [5]. Note that Theorem 2 does not state anything about the convergence of y (n) J = y (n) j ; j 2 J) In general y (n) J may not converge, as can be expected from (24) this does not matter since we infer the actual rates only from y (n) J ) Note that since ff n 0, the prices p; q ....

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K. Kar, S. Sarkar, L. Tassiulas, "Optimization Based Rate Control for Multirate Multicast Sessions", Technical Report TR 2000-21, Institute for Systems Research and University of Maryland, 2000.

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K. Kar, S. Sarkar, L. Tassiulas, "Optimization Based Rate Control for Multirate Multicast Sessions", Technical Report TR 2000-21, Institute of Systems Research and University of Maryland, 2000, www.glue.umd.edu/koushik/papers.html

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K. Kar, S. Sarkar, L. Tassiulas, Optimization based rate control for multirate multicast sessions, in: Proceedings of IEEE INFOCOM, April 2001.

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Koushik Kar, Saswati Sarkar, and Leandros Tassiulas. Optimization based rate control for multirate multicast sessions. In Proceedings of IEEE Infocom, April 2001.

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K. Kar, S. Sarkar, and L. Tassiulas, "Optimization based rate control for multirate multicast sessions," Institute of Systems Research and University of Maryland, Tech. Rep., 2000.

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K. Kar, S. Sarkar, and L. Tassiulas. Optimization based rate control for multirate multicast sessions. In Proceedings of INFOCOM, Alaska, 2001.

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K. Kar, S. Sakar, and L. Tassiulas, "Optimization based rate control for multirate multicast sessions," in Proc. IEEE INFOCOM'01, Anchorage, Alaska, USA, Apr. 2001, pp. 123--132.

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Kar, K., Sarcar, S., Tassiulas, L.: Optimization based rate control for multirate multicast sessions. In: Proceedings of Infocom 2001. (2001)

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K. Kar, S. Sarkar, and L. Tassiulas. Optimization based rate control for multirate multicast sessions. In Proceedings of IEEE Infocom, April 2001.

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K. Kar, S. Sarkar, and L. Tassiulas. Optimization based rate control for multirate multicast sessions. In IEEE INFOCOM'01, Anchorage, Alaska, April 2001.

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