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16
Flowlevel stability of data networks with nonconvex and timevarying rate regions
 In Proceedings of ACM Sigmetrics
, 2007
"... In this paper we characterize flowlevel stochastic stability for networks with nonconvex or timevarying rate regions under resource allocation based on utility maximization. Similar to prior works on flowlevel stability, we consider exogenous data arrivals with finite workloads. However, to mode ..."
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In this paper we characterize flowlevel stochastic stability for networks with nonconvex or timevarying rate regions under resource allocation based on utility maximization. Similar to prior works on flowlevel stability, we consider exogenous data arrivals with finite workloads. However, to model many realistic situations, the rate region, which constrains the feasibility of resource allocation, may be either nonconvex or timevarying. When the rate region is fixed but nonconvex, we derive sufficient and necessary conditions for stability, which coincide when the set of allocated rate vectors has continuous contours. When the rate region is timevarying according to some stationary, ergodic process, we derive the precise stability region. In both cases, the size of the stability region depends on the resource allocation policy, in particular, on the fairness parameter α in αfair utility maximization. This is in sharp contrast with the substantial existing literature on stability under fixed and convex rate regions, in which the stability region coincides with the rate region for many utilitybased resource allocation schemes, independently of the value of the fairness parameter. We further investigate the tradeoff between fairness and stability when rate region is nonconvex or timevarying. Numerical examples of both wired and wireless networks are provided to illustrate the new stability regions and tradeoffs proved in the paper.
Stochastic Network Utility Maximization A tribute to Kelly’s paper published in this journal a decade ago
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Online optimization of 802.11 mesh networks
 In Proc. of CoNEXT
, 2009
"... 802.11 wireless mesh networks are ubiquitous, but suffer from severe performance degradations due to poor synergy between the 802.11 CSMA MAC protocol and higher layers. Several solutions have been proposed that either involve significant modifications to the 802.11 MAC or legacy higher layer protoc ..."
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Cited by 7 (0 self)
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802.11 wireless mesh networks are ubiquitous, but suffer from severe performance degradations due to poor synergy between the 802.11 CSMA MAC protocol and higher layers. Several solutions have been proposed that either involve significant modifications to the 802.11 MAC or legacy higher layer protocols, or rely on 802.11 MAC models seeded with offline measurements performed during network downtime. We introduce a technique for online optimization of 802.11 wireless mesh networks using rate control at the network layer. The technique is based on a lightweight model that characterizes the feasible rates region of an operational 802.11 wireless mesh network. Unlike existing 802.11 modeling approaches, the parameters of this model can be estimated online, incur minimal overhead and can be realized using standard probing mechanisms at the network layer. Using analysis and extensive measurements over a wireless mesh network testbed, we validate the assumptions on which the model is built, and explain the principles behind the choice and estimation of its parameters. The benefits of the model and its solution in terms of fairness, throughput and stability are demonstrated operationally for a range of multihop topologies and configurations.
Asymptotically optimal parallel resource assignment with interference. Queueing Systems
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Stochastic network utility maximisation  a tribute to Kelly’s paper published in this journal a decade ago
 EUROPEAN TRANSACTIONS ON TELECOMMUNICATIONS
, 2008
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Stability, Fairness, and Performance: A FlowLevel Study on Nonconvex and TimeVarying Rate Regions
"... Abstract—The flowlevel stability and performance of data networks with utilitymaximizing allocations are studied in this paper. Similarly to prior works on flowlevel models, exogenous data arrivals with finite workloads are considered. However, to model many realistic situations, the rate region, ..."
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Abstract—The flowlevel stability and performance of data networks with utilitymaximizing allocations are studied in this paper. Similarly to prior works on flowlevel models, exogenous data arrivals with finite workloads are considered. However, to model many realistic situations, the rate region, which constrains the feasibility of resource allocation, may be either nonconvex or timevarying. When the rate region is fixed but nonconvex, sufficient and necessary conditions are characterized for stability for a class offair allocation policies, which coincide when the set of allocated rate vectors have continuous contours. When the rate region is timevarying according to a Markovian stationary and ergodic process, the precise stability region is obtained. In both cases, the size of the stability region depends on the resource allocation policy, in particular, on the fairness parameter infair utility maximization. This is in sharp contrast with the substantial existing literature on stability under fixed and convex rate regions, in which the stability region coincides with the rate region for many utilitybased resource allocation schemes, independent of the value of the fairness parameter. It is further shown that for networks which consist of flows from two different classes underfair allocations, there exists a tradeoff between the stability region and the fairness parameter. Moreover, the impact of this fairness–stability tradeoff on the system performance, e.g., average throughput and mean flow response time, is studied, and numerical experiments that illustrate the new stability region and the performance versus fairness tradeoff are presented.
On Multiplexing Flows: Does it Hurt or Not?
"... Abstract—This paper analyzes queueing behavior subject to multiplexing a stochastic process M(n) of flows, and not a constant as conventionally assumed. By first considering the case when M(n) is iid, it is shown that flows ’ multiplexing ‘hurts’ the queue size (i.e., the queue size increases in dis ..."
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Abstract—This paper analyzes queueing behavior subject to multiplexing a stochastic process M(n) of flows, and not a constant as conventionally assumed. By first considering the case when M(n) is iid, it is shown that flows ’ multiplexing ‘hurts’ the queue size (i.e., the queue size increases in distribution). The simplicity of the iid case enables the quantification of the ‘best’ and ‘worst ’ distributions of M(n), i.e., minimizing/maximizing the queue size. The more general, and also realistic, case when M(n) is Markovmodulated reveals an interesting behavior: flows ’ multiplexing ‘hurts ’ but only when the multiplexed flows are sufficiently long. An important caveat raised by such observations is that the conventional approximation of M(n) by a constant can be very misleading for queueing analysis. I.
On Capacity Dimensioning in Dynamic Scenarios: The Key Role of Peak Values
"... Abstract—This paper analyzes queueing behavior in queues with a random number of parallel flows, and not static as typically assumed. By deriving upper and lower bounds on the queue size distribution, the paper identifies extremal properties in such dynamic queues. The extremal bestcase distributio ..."
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Abstract—This paper analyzes queueing behavior in queues with a random number of parallel flows, and not static as typically assumed. By deriving upper and lower bounds on the queue size distribution, the paper identifies extremal properties in such dynamic queues. The extremal bestcase distribution (minimizing the queue) is simply the constant, whereas the worstcase distribution (maximizing the queue) has a bimodal structure. From a more practical point of view, this paper highlights an idiosyncrasy of dynamic queues: unlike in static queues whereby capacity dimensioning is dominated by averagevalues (subject to certain safety margins), in dynamic queues the capacity dimensioning is dominated instead by peakvalues. I.
6. New Results................................................................................. 3
"... d' ctivity eport 2006 Table of contents ..."
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