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105
PLM: Fast Convergence for Cumulative Layered Multicast Transmission Schemes
 In Proceedings ACM SIGMETRICS 2000
, 2000
"... A major challenge in the Internet is to deliver live audio/video content with a good quality and to transfer files to large number of heterogeneous receivers. Multicast and cumulative layered transmission are two mechanisms of interest to accomplish this task efficiently. However, protocols using ..."
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Cited by 96 (6 self)
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A major challenge in the Internet is to deliver live audio/video content with a good quality and to transfer files to large number of heterogeneous receivers. Multicast and cumulative layered transmission are two mechanisms of interest to accomplish this task efficiently. However, protocols using these mechanisms suffer from slow convergence time, lack of interprotocol fairness or TCPfairness, and loss induced by the join experiments. In this paper we define and investigate the properties of a new multicast congestion control protocol (called PLM) for audio/video and file transfer applications based on a cumulative layered multicast transmission. A fundamental contribution of this paper is the introduction and evaluation of a new and efficient technique based on packet pair to infer which layers to join. We evaluated PLM for a large variety of scenarios and show that it converges fast to the optimal link utilization, induces no loss to track the available bandwidth, has inter...
Optimization based rate control for multirate multicast sessions
, 2001
"... Multirate multicasting, where the receivers of a multicast group can receive service at different rates, is an efficient mode of data delivery for many realtime applications. In this paper, we address the problem of achieving rates that maximize the total receiver utility for multirate multicast se ..."
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Cited by 77 (9 self)
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Multirate multicasting, where the receivers of a multicast group can receive service at different rates, is an efficient mode of data delivery for many realtime applications. In this paper, we address the problem of achieving rates that maximize the total receiver utility for multirate multicast sessions. This problem not only takes into account the heterogeneity in user requirements, but also provides a unified framework for diverse fairness objectives. We propose two algorithms and prove that they converge to the optimal rates for this problem. The algorithms are distributed and scalable, and do not require the network to know the receiver utilities. We discuss how these algorithms can be implemented in a real network, and also demonstrate their convergence through simulation experiments.
Layered PeertoPeer Streaming
, 2003
"... In this paper, we propose a peertopeer streaming solution to address the ondemand media distribution problem. We identify two issues, namely the asynchrony of user requests and heterogeneity of peer network bandwidth. Our key techniques to address these two issues are cacheandrelay and layerenc ..."
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Cited by 76 (10 self)
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In this paper, we propose a peertopeer streaming solution to address the ondemand media distribution problem. We identify two issues, namely the asynchrony of user requests and heterogeneity of peer network bandwidth. Our key techniques to address these two issues are cacheandrelay and layerencoded streaming. A unique challenge of layered peertopeer streaming is that the bandwidth and data availability (number of layers received) of each receiving peer are constrained and heterogeneous, which further limits the bandwidth and data availability of its downstream node when it acts as the supplying peer. This challenge distinguishes our work from existing studies on layered multicast. Our experiments show that our solution is e#cient at utilizing bandwidth resource of supplying peers, scalable at saving server bandwidth consumption, and optimal at maximizing streaming qualities of all peers.
MLDA: A TCPfriendly Congestion Control Framework for Heterogeneous Multicast Environments
 In Proceedings IWQoS 2000
, 2000
"... To avoid overloading the Internet and starving TCP connections, multimedia flows using noncongestion controlled UDP need to be enhanced with congestion control mechanisms. In this paper, we present a general framework for achieving TCPfriendly congestion control called MLDA. Using MLDA, multimedia ..."
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Cited by 63 (2 self)
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To avoid overloading the Internet and starving TCP connections, multimedia flows using noncongestion controlled UDP need to be enhanced with congestion control mechanisms. In this paper, we present a general framework for achieving TCPfriendly congestion control called MLDA. Using MLDA, multimedia senders adjust their transmission rate in accordance with the network congestion state. For taking the heterogeneity of the Internet and the end systems into account, MLDA supports layered data transmission where the shape and number of the layers is determined dynamically based on feedback information generated by the receivers. Further, we discuss a measurement approach that allows receivers in large multicast sessions to estimate the round trip delay estimation to the sender in a scalable way. For exchanging control information between the sender and receivers we investigate the possibility of using the real time transport protocol (RTP) and discuss the required changes in order for RTP ...
Priority Service and MaxMin Fairness
, 2003
"... We study a priority service where users are free to choose the priority of their traffic, but are charged accordingly by the network. We assume that each user chooses priorities to maximize its own net benefit, and model the resulting interaction among users as a noncooperative game. We show that t ..."
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Cited by 62 (1 self)
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We study a priority service where users are free to choose the priority of their traffic, but are charged accordingly by the network. We assume that each user chooses priorities to maximize its own net benefit, and model the resulting interaction among users as a noncooperative game. We show that there exists an unique equilibrium for this game and that in equilibrium the bandwidth allocation is weighted maxmin fair.
A Unified Framework for MaxMin and MinMax Fairness with Applications
 in Proceedings of 40th Annual Allerton Conference on Communication, Control, and Computing
, 2002
"... Maxmin fairness is widely used in various areas of networking. In every case where it is used, there is a proof of existence and one or several algorithms for computing the maxmin fair allocation; in most, but not all cases, they are based on the notion of bottlenecks. In spite of this wide app ..."
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Cited by 60 (1 self)
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Maxmin fairness is widely used in various areas of networking. In every case where it is used, there is a proof of existence and one or several algorithms for computing the maxmin fair allocation; in most, but not all cases, they are based on the notion of bottlenecks. In spite of this wide applicability, there are still examples, arising in the context of mobile or peertopeer networks, where the existing theories do not seem to apply directly. In this paper, we give a unifying treatment of maxmin fairness, which encompasses all existing results in a simplifying framework, and extends its applicability to new examples. First, we observe that the existence of maxmin fairness is actually a geometric property of the set of feasible allocations (uniqueness always holds). There exist sets on which maxmin fairness does not exist, and we describe a large class of sets on which a maxmin fair allocation does exist. This class contains the compact, convex sets of , but not only. Second, we give a general purpose, centralized algorithm, called Maxmin Programming, for computing the maxmin fair allocation in all cases where it exists (whether the set of feasible allocations is in our class or not). Its complexity is of the order of linear programming steps in , in the case where the feasible set is defined by linear constraints. We show that, if the set of feasible allocations has the freedisposal property, then Maxmin Programming degenerates to a simpler algorithm, called Water Filling, whose complexity is much less. Free disposal corresponds to the cases where a bottleneck argument can be made, and Water Filling is the general form of all previously known centralized algorithms for such cases. Our derivations are based on the relation betwe...
Interreceiver fair multicast communication over the Internet
 in Proc. NOSSDAVâ€™99
, 1999
"... Multicast protocols target applications involving a large number of receivers with heterogeneous data reception capabilities. To accommodate heterogeneity, the sender may transmit at multiple rates, requiring mechanisms to determine the rates and allocate receivers to rates. In this paper, we develo ..."
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Cited by 37 (1 self)
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Multicast protocols target applications involving a large number of receivers with heterogeneous data reception capabilities. To accommodate heterogeneity, the sender may transmit at multiple rates, requiring mechanisms to determine the rates and allocate receivers to rates. In this paper, we develop a protocol to control the rates of a multicast session, with the goal of maximizing the interreceiver fairness, an intrasession measure that captures the collective \satisfaction " of the session receivers. Our target environment is the Internet, where fair sharing of bandwidth must be achieved via endsystem mechanisms and fairness to TCP is important. We develop and evaluate protocols to maximize this measure by maintaining a xedrate base group and a variablerate group. We show that our schemes o er improvement over singlerate sessions, while maintaining TCPfriendliness. 1
Optimal Partitioning of Multicast Receivers
, 2000
"... Multicast sessions may have a large number of receivers with heterogeneous reception capacities. To accommodate this heterogeneity, various multirate schemes, based upon the use of layering or replication, have been proposed. We consider in this paper the optimal partitioning of receivers into grou ..."
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Cited by 31 (3 self)
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Multicast sessions may have a large number of receivers with heterogeneous reception capacities. To accommodate this heterogeneity, various multirate schemes, based upon the use of layering or replication, have been proposed. We consider in this paper the optimal partitioning of receivers into groups for multirate schemes. For a general class of utility functions, we formulate the partitioning problem as an optimization problem to maximize the sum of receiver utilities. We present an efficient dynamic programming algorithm to solve the partitioning problem, and prove that the solution it finds is optimal. We also show that the majority of the benefit of a multirate scheme can be gained by using a small number of groups (or layers), say 4 to 5. To illustrate our solution approach, we apply it to the case where receiver capacities are determined by multirate maxmin fair rates. A complete protocol for receiver rates computation, rates collection, optimal receiver partitioning, and receiver adaptation is presented. We then compare our approach with other multirate approaches as well as a singlerate approach. Experimental results show that our approach provides substantial performance improvements.
Fair Allocation of Utilities in Multirate Multicast Networks
, 1999
"... We study fairness in a multicast network. We assume that the source hierarchically encodes its signal and the hierarchical structure is predetermined. We study fair allocation of utilities, where utility of a bandwidth can be the number of layers or the bandwidth itself, or any other function of ..."
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Cited by 29 (10 self)
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We study fairness in a multicast network. We assume that the source hierarchically encodes its signal and the hierarchical structure is predetermined. We study fair allocation of utilities, where utility of a bandwidth can be the number of layers or the bandwidth itself, or any other function of the bandwidth depending on system requirements. The utility function is not strictly increasing in general. Fairness issues become vastly different in this case as opposed to that when the utility function is strictly increasing. Computation of lexicographically optimal utility allocation becomes NPhard in this case, while lexicographically optimal utility allocation is polynomial complexity computable when the utility function is strictly increasing. Furthermore, maxmin fair utility allocation may not exist in the general case. We introduce a new notion of fairness, maximal fairness. We propose a polynomial complexity algorithm for computation of maximally fair utility allocation. Even though, maximal fairness is a weaker notion of fairness, it coincides with lexicographic optimality and maxmin fairness, when maxmin fair utility allocation exists. So the algorithm for computing maximally utility allocation computes maxmin fair utility allocation, when the latter exists.
Back Pressure Based Multicast Scheduling for Fair Bandwidth Allocation
, 2001
"... We study fair allocation of resources in multicast networks with multirate capabilities. In multirate transmission, the session source hierarchically encodes its signal and the receivers subscribe to the appropriate number of layers. The objective of the network is to distribute the layers fairl ..."
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Cited by 25 (3 self)
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We study fair allocation of resources in multicast networks with multirate capabilities. In multirate transmission, the session source hierarchically encodes its signal and the receivers subscribe to the appropriate number of layers. The objective of the network is to distribute the layers fairly. This can be attained either by computing the fair rates first, and then using a scheduling policy to attain the fair rates, or by using a scheduling policy which allocates the fair rates without computing them explicitly. The first requires knowledge of system parameters like link bandwidth, which are not generally known to the link schedulers. The second approach is more realistic. We present a scheduling policy which allocates the fair rates without computing them beforehand. We have presented analytical and experimental results demonstrating the fairness of the resulting rate allocation. In addition to guaranteeing the fair rates, this policy confines the packet losses to enhancement layers, and protects the more important base layers, when there is shortage of bandwidth. Furthermore, this policy does not require any knowledge of traffic statistics, is computationally simple, and is essentially local information based.