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34
Hopbyhop Congestion Control over a Wireless MultiHop Network
, 2004
"... This paper focuses on congestion control over multihop, wireless networks. In a wireless network, an important constraint that arises is that due to the MAC (Media Access Control) layer. Many wireless MACs use a timedivision strategy for channel access, where, at any point in space, the physical ch ..."
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Cited by 96 (0 self)
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This paper focuses on congestion control over multihop, wireless networks. In a wireless network, an important constraint that arises is that due to the MAC (Media Access Control) layer. Many wireless MACs use a timedivision strategy for channel access, where, at any point in space, the physical channel can be accessed by a single user at each instant of time. In this paper, we develop a fair hopbyhop congestion control algorithm with the MAC constraint being imposed in the form of a channel access time constraint, using an optimization based framework. In the absence of delay, we show that this algorithm are globally stable using a Lyapunov function based approach. Next, in the presence of delay, we show that the hopbyhop control algorithm has the property of spatial spreading. In other words, focused loads at a particular spatial location in the network get "smoothed" over space. We derive bounds on the "peak load" at a node, both with hopbyhop control, as well as with endtoend control, show that significant gains are to be had with the hopbyhop scheme, and validate the analytical results with simulation.
Maxmin Fair Scheduling in Wireless Networks
, 2002
"... We consider scheduling policies for maxmin fair allocation of bandwidth in wireless adhoc networks. We formalize the maxmin fair objective under wireless scheduling constraints. We propose a fair scheduling which assigns dynamic weights to the flows such that the weights depend on the congestion in ..."
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Cited by 83 (4 self)
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We consider scheduling policies for maxmin fair allocation of bandwidth in wireless adhoc networks. We formalize the maxmin fair objective under wireless scheduling constraints. We propose a fair scheduling which assigns dynamic weights to the flows such that the weights depend on the congestion in the neighborhood and schedule the flows which constitute a maximum weighted matching. It is possible to analytically prove that this policy attains both short term and long term fairness. We consider more generalized fairness notions, and suggest mechanisms to attain these objectives. I.
Endtoend bandwidth guarantees through fair local spectrum share in wireless ad hoc networks
 Proc. Control and Decision Conference (CDC) 2003
"... endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution m ..."
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Cited by 33 (4 self)
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endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubspermissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
Any Workconserving Policy Stabilizes the Ring with Spatial Reuse
"... We consider the ring network with spatial reuse. Traffic streams may enter and exit the network at any node. We adopt an arrival traffic model with deterministic constraints on its sample paths, which conforms to the output traffic of a leaky bucket rate control mechanism. A transmission policy sp ..."
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Cited by 28 (2 self)
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We consider the ring network with spatial reuse. Traffic streams may enter and exit the network at any node. We adopt an arrival traffic model with deterministic constraints on its sample paths, which conforms to the output traffic of a leaky bucket rate control mechanism. A transmission policy specifies at each time which traffic stream will be transmitted at the outgoing link by each node. We provide an upper bound on the asymptotic backlog of the ring that holds for all workconserving policies and is independent of the initial conditions. This bound remains finite as long as the maximum load of every link is less than one. The latter condition is also necessary for the existence of an asymptotic bound that is independent of the initial conditions.
Maxmin fair scheduling in wireless ad hoc networks
 IEEE J. SEL. AREAS COMMUN
, 2005
"... We investigate from an algorithmic perspective the maxmin fair allocation of bandwidth in wireless ad hoc networks. We formalize the maxmin fair objective under wireless scheduling constraints, and present a necessary and sufficient condition for maxmin fairness of a bandwidth allocation. We propos ..."
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Cited by 19 (2 self)
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We investigate from an algorithmic perspective the maxmin fair allocation of bandwidth in wireless ad hoc networks. We formalize the maxmin fair objective under wireless scheduling constraints, and present a necessary and sufficient condition for maxmin fairness of a bandwidth allocation. We propose an algorithm that assigns weights to the sessions dynamically such that the weights depend on the congestion in the neighborhood, and schedules the sessions that constitute a maximum weighted matching. We prove that this algorithm attains the maxmin fair rates, even though it does not use any information about the statistics of the packet arrival process.
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 17 (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.
A Framework for Routing and Congestion Control in Multicast Networks
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 1999
"... We propose a new multicast routing and scheduling algorithm called multipurpose multicast routing and scheduling algorithm (MMRS). The routing policy load balances amongst various possible routes between the source and the destinations, basing its decisions on the message queue lengths at the source ..."
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Cited by 16 (3 self)
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We propose a new multicast routing and scheduling algorithm called multipurpose multicast routing and scheduling algorithm (MMRS). The routing policy load balances amongst various possible routes between the source and the destinations, basing its decisions on the message queue lengths at the source node. The scheduling amongst various sessions sharing links is devised such that the flow of a session depends on the congestion of the next hop links. MMRS is throughput optimal and computationally simple. It can be implemented in a distributed, asynchronous manner. It has several parameters which can be suitably modified to control the end to end delay, packet loss in a topology specific manner. These parameters can be adjusted to offer limited priorities to some desired sessions. MMRS is expected to play a significant role in end to end congestion control in the multicast scenario.
Fast matching algorithms for repetitive optimization: An application to switch scheduling
 IN PROCEEDINGS OF THE 40TH ANNUAL CONFERENCE ON INFORMATION SCIENCES AND SYSTEMS (CISS
, 2006
"... Scheduling in an input buffered switch can be viewed as repeated matching (corresponding to once every time slot) in a bipartite graph. It has been shown that scheduling algorithms based on maximum weight matching (MWM) with queuelengths as the weights, leads to excellent performance in terms of t ..."
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Cited by 11 (1 self)
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Scheduling in an input buffered switch can be viewed as repeated matching (corresponding to once every time slot) in a bipartite graph. It has been shown that scheduling algorithms based on maximum weight matching (MWM) with queuelengths as the weights, leads to excellent performance in terms of throughput and delay. However, computing MWM using a strongly polynomial time algorithm requires O(n 3) operations in an n×n switch. The main motivation for this paper comes from the following two observations: (1) The weights of edges (packets in buffer) change only a little between successive time slots, thus changing the weight of the MWM only by a small amount; (2) Under MWM algorithm, the average queuesizes are small. The main difficulty in utilizing these properties comes from the fact that small changes in weights can change the matching arbitrarily, thus making it hard for current popular algorithms to compute an MWM quickly using the information from past (or memory). In this paper, we develop an algorithm based on the algorithm of Cunningham and Marsh [1] that uses the above two properties in order to to find the new MWM quickly. Specifically, for an n port inputqueued switch, i.e. a switch with n inputs and n outputs, our algorithm finds MWM in O(n²) operations using past information. We believe that the incremental nature of our algorithm may be useful in the context of other applications.
Throughputoptimal Scheduling in Multichannel Access Point Networks under TimeVarying Channel Rates
, 2005
"... We consider the problem of uplink/downlink scheduling in a multichannel wireless access point network where channel states differ across channels as well as users, vary with time, and can be measured only infrequently. We demonstrate that, unlike infrequent measurement of queue lengths, infrequent m ..."
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Cited by 8 (1 self)
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We consider the problem of uplink/downlink scheduling in a multichannel wireless access point network where channel states differ across channels as well as users, vary with time, and can be measured only infrequently. We demonstrate that, unlike infrequent measurement of queue lengths, infrequent measurement of channel states reduce the maximum attainable throughput. We then prove that in frequency division multiplexed systems, a dynamic scheduling policy that depends on both the channel rates (averaged over the measurement interval) and the queue lengths, attains the maximum possible throughput. We also generalize the scheduling policy to solve the joint power allocation and scheduling problem in orthogonal frequency division multiplexed systems. In addition, we provide simulation studies that demonstrate the impact of the frequency of channel and queue state measurements on the average delay and attained throughput. Index Terms Throughputoptimal scheduling, Multichannel access point networks, Infrequent channel measurements.
Stability and Asymptotic Optimality of Generalized MaxWeight Policies
, 2007
"... It is shown that stability of the celebrated MaxWeight or back pressure policies is a consequence of the following interpretation: either policy is myopic with respect to a surrogate value function of a very special form, in which the “marginal disutility ” at a buffer vanishes for vanishingly small ..."
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Cited by 7 (2 self)
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It is shown that stability of the celebrated MaxWeight or back pressure policies is a consequence of the following interpretation: either policy is myopic with respect to a surrogate value function of a very special form, in which the “marginal disutility ” at a buffer vanishes for vanishingly small buffer population. This observation motivates the hMaxWeight policy, defined for a wide class of functions h. These policies that share many of the attractive properties of the MaxWeight policy: (i) The policy does not require arrival rate data. (ii) The hmyopic policy is stabilizing when h is a perturbation of a monotone linear function, or a monotone Lyapunov function for the fluid model. (iii) A perturbation of the relative value function for a workload relaxation gives rise to a myopic policy that is approximately averagecost optimal in heavy traffic, with logarithmic regret. The first results are obtained for a completely general stochastic network model. Asymptotic optimality is established for the general scheduling model with a single bottleneck.