QoS-aware bandwidth provisioning for IP network links

Berg, Hans van den; Mandjes, Michel; Meent, R. van de; Pras, Aiko; Roijers, Frank; Venemans, P.J.

doi:10.1016/j.comnet.2005.05.028

Cited by 40 publications

(63 citation statements)

References 17 publications

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“…The present paper builds upon previous work on traffic modeling and network link dimensioning [8,9,11,12]. Section 2 recapitulates our findings on the modeling of real network traffic (based on our measurements at 5 representative networking environments); importantly, these measurements indicate that under fairly general circumstances the Gaussian traffic model applies.…”

Section: Introductionsupporting

confidence: 59%

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Smart Dimensioning of IP Network Links

Meent

Mandjes

Pras

2007

Managing Virtualization of Networks and Services

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Abstract. Link dimensioning is generally considered as an effective and (operationally) simple mechanism to meet (given) performance requirements. In practice, the required link capacity C is often estimated by rules of thumb, such aswhere M is the (envisaged) average traffic rate, and d some (empirically determined) constant larger than 1. This paper studies the viability of this class of 'simplistic' dimensioning rules. Throughout, the performance criterion imposed is that the fraction of intervals of length T in which the input exceeds the available output capacity (i.e., C · T ) should not exceed ε, for given T and ε.We first present a dimensioning formula that expresses the required link capacity as a function of M and a variance term V (T ), which captures the burstiness on timescale T . We explain how M and V (T ) can be estimated with low measurement effort. The dimensioning formula is then used to validate dimensioning rules of the type C = d · M. Our main findings are: (i) the factor d is strongly affected by the nature of the traffic, the level of aggregation, and the network infrastructure; if these conditions are more or less constant, one could empirically determine d; (ii) we can explicitly characterize how d is affected by the 'performance parameters', i.e., T and ε.

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Section: Introductionsupporting

confidence: 59%

“…In this paper we rely on the notion of link transparency that was introduced in [11]. Its main objective is to ensure that the links are more or less 'transparent' to the users, in that the users should not (or almost never) perceive any performance degradation due to a lack of bandwidth.…”

Section: Derivation Of Link Dimensioning Formula For Gaussian Trafficmentioning

confidence: 99%

Smart Dimensioning of IP Network Links

Meent

Mandjes

Pras

2007

Managing Virtualization of Networks and Services

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“…Aggregated traffic shows characteristics in multiple time scales [11], [17], [36]. Based on the time-stamp and the size of each packet, the variability of the traffic rates can be observed in time scale ranging from the accuracy level of the time-stamps well below 1 ms up to the 30 minutes length of the traces.…”

Section: Traffic Tracesmentioning

confidence: 99%

Packet Loss in Real-Time Services: Markovian Models Generating QoE Impairments

Hohlfeld

Geib

Hasslinger

2008

2008 16th Interntional Workshop on Quality of Service

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Abstract-Real-time Internet services are gaining in popularity due to rapid provisioning of broadband access technologies. Delivery of high Quality of Experience (QoE) is important for consumer acceptance of multimedia applications.IP packet level errors affect QoE and the resulting quality degradations have to be taken into account in network operation. We derive the second order statistics of the number of packet losses in finite Markov models over several relevant time scales and adapt them to loss processes visible in wired and wireless transmission channels.Higher order Markov chains offer a large set of parameters to be exploited by complex fitting procedures. We experience that the 2-state Gilbert-Elliott model already captures a wide range of observed loss pattern appropriately and discuss how such models can be used to examine the quality degradations caused by packet losses.

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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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QoS-aware bandwidth provisioning for IP network links

Abstract: C e n t r u m v o o r W i s k u n d e e n I n f o r m a t i c a PNA Probability, Networks and AlgorithmsProbability, Networks and Algorithms

Cited by 40 publications

References 17 publications

Smart Dimensioning of IP Network Links

Smart Dimensioning of IP Network Links

Packet Loss in Real-Time Services: Markovian Models Generating QoE Impairments

The multi-source model for dimensioning data networks

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