Evaluation of 'user-oriented' and 'black-box' traffic models for link provisioning

Meent, R. van de; Mandjes, Michel

doi:10.1109/ngi.2005.1431691

Cited by 10 publications

(10 citation statements)

References 16 publications

(23 reference statements)

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“…A tcpdump record consists of a precise timestamp (of observing the packet at the measurement point) and (in our case) the headers of the packets up to the transport protocol layer. In total, some 700 traces were recorded; see Table I for an overview of the measurements 1 . Also listed in Table I is the estimated number of local hosts at each location, and the average aggregate bandwidth utilization over all traces.…”

Section: Description Of the Datasetsmentioning

confidence: 99%

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Gaussian traffic everywhere?

Meent

Mandjes²,

Pras

2006

2006 IEEE International Conference on Communications

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Section: Description Of the Datasetsmentioning

confidence: 99%

“…For instance, by having a thorough understanding of the offered traffic, it is possible to perform adequate bandwidth provisioning (i.e., to assign adequate bandwidth capacity figures in an economically viable way, at the same time satisfying a certain desired performance criterion, see, e.g. [1]). …”

Section: Introductionmentioning

confidence: 99%

Gaussian traffic everywhere?

Meent

Mandjes²,

Pras

2006

2006 IEEE International Conference on Communications

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“…We now verify whether this applies for our data set {U, R, C, A, S}. As a measure of Gaussianity we use the notion of the linear correlation coefficient as the goodness-of-fit measure, as in [15], [17]; in [21] it is shown that the use of this statistic leads to similar conclusions as the Kolmogorov-Smirnov test that was used in [12]. Fig.…”

Section: B Gaussianity For Different Levels Of Horizontal and Verticmentioning

confidence: 73%

Resource Dimensioning Through Buffer Sampling

Mandjes

Meent

2009

IEEE/ACM Trans. Networking

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“…So-called 'user-oriented' and 'black box' traffic models are used to characterize measurement results. They are evaluated in [37] with regard to their accuracy for link provisioning. It turned out that black box models are easier to estimate and yield accurate provisioning guidelines.…”

Section: Capacity (Over-)provisioningmentioning

confidence: 99%

Capacity overprovisioning for networks with resilience requirements

Menth

Martin

Charzinski

2006

Proceedings of the 2006 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications

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This work focuses on capacity overprovisioning (CO) as an alternative to admission control (AC) to implement quality of service (QoS) in packet-switched communication networks. CO prevents potential overload while AC protects the QoS of the traffic during overload situations. Overload may be caused, e.g., by fluctuations of the traffic rate on a link due to its normal stochastic behavior (a), by traffic shifts within the network due to popular contents (b), or by redirected traffic due to network failures (c). Capacity dimensioning methods for CO need to take into account all potential sources of overload while AC can block excess traffic caused by (a) and (b) if the capacity does not suffice. The contributions of this paper are (1) the presentation of a capacity dimensioning method for networks with resilience requirements and changing traffic matrices, (2) the investigation of the impact of the mentioned sources of overload (a-c) on the required capacity for CO in networks with and without resilience requirements, and (3) a comparison of this required capacity with the one for AC. Our results show that in the presence of strong traffic shifts CO requires more capacity than AC. However, if resilience against network failures is required, both CO and AC need additional backup capacity for the redirected traffic. In this case, CO can use the backup capacity to absorb other types of overload. As a consequence, CO and AC have similar bandwidth requirements. These findings are robust against the network size.

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Evaluation of 'user-oriented' and 'black-box' traffic models for link provisioning

Cited by 10 publications

References 16 publications

Gaussian traffic everywhere?

Gaussian traffic everywhere?

Resource Dimensioning Through Buffer Sampling

Capacity overprovisioning for networks with resilience requirements

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