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A queueing analysis of maxmin fairness, proportional fairness and balanced fairness
, 2006
"... We compare the performance of three usual allocations, namely maxmin fairness, proportional fairness and balanced fairness, in a communication network whose resources are shared by a random number of data flows. The model consists of a network of processorsharing queues. The vector of service rates ..."
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Cited by 67 (13 self)
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We compare the performance of three usual allocations, namely maxmin fairness, proportional fairness and balanced fairness, in a communication network whose resources are shared by a random number of data flows. The model consists of a network of processorsharing queues. The vector of service rates, which is constrained by some compact, convex capacity set representing the network resources, is a function of the number of customers in each queue. This function determines the way network resources are allocated. We show that this model is representative of a rich class of wired and wireless networks. We give in this general framework the stability condition of maxmin fairness, proportional fairness and balanced fairness and compare their performance on a number of toy networks.
Dimensioning of wireless mesh networks with flowlevel QoS requirements
 in The 3rd ACM International Workshop on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks (PEWASUN 2006
, 2006
"... We study dimensioning of wireless mesh networks with elastic traffic subject to flowlevel performance constraints. The objective of the dimensioning is to provide orderofmagnitude estimates of the required transmission resources in the network. The results can be used to evaluate the feasibility ..."
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Cited by 4 (0 self)
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We study dimensioning of wireless mesh networks with elastic traffic subject to flowlevel performance constraints. The objective of the dimensioning is to provide orderofmagnitude estimates of the required transmission resources in the network. The results can be used to evaluate the feasibility and sensitivity of a given network scenario, consisting of node locations, the interference model and the traffic matrix. We apply a network model which incorporates the dynamic flowlevel behavior and MAC layer wireless interference. We formulate the dimensioning problems for two capacity parameters resulting from different interference models. Furthermore, three approximation schemes are presented to facilitate the computations. The approaches are illustrated by numerical examples.
Insensitive Traffic Splitting in Data Networks
, 2005
"... Bonald et al. have studied insensitivity in data networks assuming a fixed route for each flow class. If capacity allocation and routing are balanced and the capacity of a given class is shared equally between the flows, the network state distribution and flow level performance are insensitive to a ..."
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Cited by 3 (2 self)
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Bonald et al. have studied insensitivity in data networks assuming a fixed route for each flow class. If capacity allocation and routing are balanced and the capacity of a given class is shared equally between the flows, the network state distribution and flow level performance are insensitive to any detailed traffic characteristics except the traffic loads. In this paper, we consider optimal insensitive load balancing executed at packet level so that the traffic of each flow may be split over several routes. Similarly to the case with fixed routing, the most efficient capacity allocation and traffic splitting policy can be determined recursively. We formulate the problem as an LP problem using either a set of predefined routes or arbitrary routes and present numerical results for two toy networks. Traffic splitting gives a clear performance improvement when compared to flow level balancing or fixed shortest path routing.
TELECTRONICS II Research Programme Final Report. FIT – Future Internet: Traffic Handling and Performance Analysis
"... The FIT project has addressed a number of problems arising in controlling the traffic and providing quality of service in the Internet and in analyzing the performance of the system. An efficient recursive algorithm has been developed for calculating the flow level performance of elastic traffic und ..."
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The FIT project has addressed a number of problems arising in controlling the traffic and providing quality of service in the Internet and in analyzing the performance of the system. An efficient recursive algorithm has been developed for calculating the flow level performance of elastic traffic under a bandwidth sharing scheme called balanced fairness (BF). The notion of BF has been extended and applied to the flow throughput estimation in ad hoc networks; also other applications of BF are being explored. Scheduling mechanisms, in general, and adaptive scheduling mechanism with delay bounds, in particular, were studied. A simple adaptive and distributed load balancing mechanism has been suggested and analyzed. In this system, traffic is gradually redistributed based on measured link loads, leading to a nearly optimal performance. Analytical results have been obtained on the performance of MAC protocols in ring networks employing optical burst switching. For traffic matrix estimation, the GraveyVaton method has been analyzed in detail in the case of Gaussian traffic variations. The stability problem of an overloaded network with measurementbased admission control has been analytically solved. I.