On expressing networks with flow transformations in convolution-form

Ciucu, Florin; Schmitt, Jens; Wang, Hao

doi:10.1109/infcom.2011.5935003

Cited by 19 publications

(31 citation statements)

References 25 publications

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“…Denoting this indicator function for bit i as Xi, we define the scaling element as a random process X = (Xi) i≥1 (a more general definition can be found in [4]). We denote the scaled arrivals as A X (t) and…”

Section: Modelling the Demultiplexing Of Variable Length Packet Flowsmentioning

confidence: 99%

“…One is, after being served by each node the output is always packets, i.e., the bits are packetized by a packetizer P L . The other is, there is no packetizer after service, yet we observe from the bit flow the last bit of each packet according to a packet process L. In previous work [4] we derive the end-to-end delay bound for the bit flow under flow transformation. The second case can be a critical challenge for that approach (L-modulated scaling process and sampling due to L).…”

Section: Definition 2 (Packet Scaling Element)mentioning

confidence: 99%

“…Previous work in deterministic network calculus has proposed the so-called data scaling element [7] to model such flow transformation. A subsequent work then introduced the stochastic scaling element [4], which represents the network in convolution-form and provides a flexible means of capturing actual transformations. The key idea therein is to commute the scaling element with a dynamic server element recursively in order to obtain a single-node form representation of the network.…”

Section: Introductionmentioning

confidence: 99%

“…Yet, for the n-node network to preserve the convolution-form the computation of the performance measures turned out to be hard. Therefore, in this paper, we commute the packet-level scaling element and the dynamic server (as proposed for the bit level in [4]), in order to provide a tractable end-to-end delay bound computation.…”

Section: Introductionmentioning

confidence: 99%

“…In Section 3, we present two methods to compute the end-to-end delay bounds. The first is a relatively direct extension of [4], whereas the second provides a more detailed treatment of the packetization of flows. In Section 4 we compare two methods and compare them against simulation results.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

End-to-End Delay Bounds for Variable Length Packet Transmissions under Flow Transformations

Wang¹,

Schmitt²

2015

Proceedings of the 8th International Conference on Performance Evaluation Methodologies and Tools

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A fundamental contribution of network calculus is the convolution-form representation of networks which enables tight end-to-end delay bounds. Recently, this has been extended to the case where the data flow is subject to transformations on its way to the destination. Yet, the extension, based on so-called scaling elements, only applies to a setting of identically sized data units, e.g., bits. In practice, of course, one often has to deal with variable-length packets. Therefore, in this paper, we address this case and propose two novel methods to derive delay bounds for variable-length packets subject to flow transformations. One is a relatively direct extension of existing work and the other one represents a more detailed treatment of packetization effects. In a numerical evaluation, we show the clear superiority of the latter one and also validate the bounds by simulation results.

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Section: Modelling the Demultiplexing Of Variable Length Packet Flowsmentioning

confidence: 99%

Section: Definition 2 (Packet Scaling Element)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

End-to-End Delay Bounds for Variable Length Packet Transmissions under Flow Transformations

Wang¹,

Schmitt²

2015

Proceedings of the 8th International Conference on Performance Evaluation Methodologies and Tools

Self Cite

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Network calculus based QoS analysis of network coding in Cluster-tree wireless sensor network

Jiang

Chen

2012

2012 Computing, Communications and Applications Conference

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A Guide to the Stochastic Network Calculus

Fidler

Rizk

2015

IEEE Commun. Surv. Tutorials

168

135

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The aim of the stochastic network calculus is to comprehend statistical multiplexing and scheduling of non-trivial traffic sources in a framework for end-to-end analysis of multinode networks. To date, several models, some of them with subtle yet important differences, have been explored to achieve these objectives. Capitalizing on previous works, this paper contributes an intuitive approach to the stochastic network calculus, where we seek to obtain its fundamental results in the possibly easiest way. In detail, the method that is assembled in this work uses moment generating functions, known from the theory of effective bandwidths, to characterize traffic arrivals and network service. Thereof, affine envelope functions with an exponentially decaying overflow profile are derived to compute statistical end-to-end backlog and delay bounds for networks.

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On expressing networks with flow transformations in convolution-form

Cited by 19 publications

References 25 publications

End-to-End Delay Bounds for Variable Length Packet Transmissions under Flow Transformations

End-to-End Delay Bounds for Variable Length Packet Transmissions under Flow Transformations

Network calculus based QoS analysis of network coding in Cluster-tree wireless sensor network

A Guide to the Stochastic Network Calculus

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