Dimensioning methods for data networks with flow-level QoS requirements

Coronado, Etienne S.; Cherkaoui, Soumaya

doi:10.1109/lcn.2007.27

Cited by 5 publications

(4 citation statements)

References 25 publications

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“…This article provides a unified treatment of data network dimensioning by summarising, validating and extending our preliminary efforts [5] and [6]. We introduce easyto-use methods (the so-called lower bound (LB) and store and forward (SF) methods) for obtaining theoretically wellfounded approximations on how much capacity is required to meet the given per-flow throughput targets for data traffic and, additionally, provide ns2 simulations to further validate the methods.…”

Section: Introductionmentioning

confidence: 94%

“…As a new theoretical result we give a proof on the convergence of the SF method. Finally, we show how the methods from Reference [6] can be extended to account for access rate limitations and we discuss how the methods can be applied to provide rough estimates of the required capacities in multiservice networks which carries both real-time and elastic traffic.…”

Section: Introductionmentioning

confidence: 99%

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Dimensioning of data networks: a flow‐level perspective

Lassila¹,

Penttinen²,

Virtamo³

2008

Trans Emerging Tel Tech

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SUMMARYTraditional network dimensioning formulations have applied the Erlang model where the connections reserve capacity in the network. Until recently, tractable stochastic network models where the connections share the capacity in the network did not exist. The latter are becoming increasingly important as they can be applied to characterise file transfers in current data networks (e.g. IP networks). In particular, they can be utilised for dimensioning of networks with respect to the file transfer performance. To this end, we consider a model where the traffic consists of elastic flows (i.e. file transfers). Flows arrive randomly and share the network resources resulting in stochastically varying transmission rates for flows. Our contribution is to develop efficient methods for capacity planning to meet the performance requirements expressed in terms of the average transmission rate of flows on a given route, i.e. the per-flow throughput. These methods are validated using ns2 simulations. We discuss also the effects of access rate limitations and how to combine the elastic traffic requirements with those of real-time traffic. Finally, we outline how the methods can be applied in wireless mesh networks. Our results enable a simple characterisation of the order-of-magnitude of the required capacities, which can be utilised as a first step in practical network planning and dimensioning.

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Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Dimensioning of data networks: a flow‐level perspective

Lassila¹,

Penttinen²,

Virtamo³

2008

Trans Emerging Tel Tech

Self Cite

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“…In fact, the flow-level models of data networks can be considered as the analogues for the Erlang model of telephone networks and its extensions to multi-rate circuit-switched networks [5]. They have proved essential for both dimensioning [3,6,29,25,24,16] and traffic engineering [15,23,21,28].…”

Section: Introductionmentioning

confidence: 99%

The multi-source model for dimensioning data networks

Bonald

Comte

2016

Computer Networks

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Traffic modeling is key to the dimensioning of data networks. Usual models rely on the implicit assumption that each user generates data flows in series, one after the other, the ongoing flows sharing equitably the considered network link. We relax this assumption and consider the more realistic case where users may generate several data flows in parallel, these flows having to share the user's access line as well. We qualify this model as multi-source since each user now behaves as an independent traffic source. Usual performance metrics like mean throughput and congestion rate must now be defined at user level rather than at flow level. We derive explicit expressions for these performance metrics under the assumption that flows share bandwidth according to balanced fairness. These results are compared with those obtained by simulation when max-min fairness is imposed, either at flow level or at user level.

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“…In fact, the flow-level models of data networks can be considered as the analogues for the Erlang model of telephone networks and its extensions to multi-rate circuit-switched networks [4]. They have proved essential for both dimensioning [3], [6], [21], [20], [14] and traffic engineering [13], [19], [17].…”

Section: Introductionmentioning

confidence: 99%

Traffic Models for User-Level Performance Evaluation in Data Networks

Bonald¹

2015

2015 27th International Teletraffic Congress

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Traffic modeling is key to the capacity planning of data networks. Usual models rely on the implicit assumption that each user generates data flows in series, one after the other, the ongoing flows sharing equitably the considered backhaul link. We relax this assumption and consider the more realistic case where users may generate several data flows in parallel, these flows having to share the user's access line as well. We derive explicit user-level performance metrics like mean throughput and congestion rate in this context, assuming balanced fair sharing between ongoing flows. These results generalize existing ones in that both match in the limit of an infinite number of access lines.

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Dimensioning methods for data networks with flow-level QoS requirements

Cited by 5 publications

References 25 publications

Dimensioning of data networks: a flow‐level perspective

Dimensioning of data networks: a flow‐level perspective

The multi-source model for dimensioning data networks

Traffic Models for User-Level Performance Evaluation in Data Networks

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