research-article

Improving Delay Bounds in the Stochastic Network Calculus by Using less Stochastic Inequalities

Authors:

Jens SchmittAuthors Info & Claims

VALUETOOLS '20: Proceedings of the 13th EAI International Conference on Performance Evaluation Methodologies and Tools

Pages 96 - 103

https://doi.org/10.1145/3388831.3388848

Published: 29 May 2020 Publication History

Abstract

Stochastic network calculus is a versatile framework to derive probabilistic end-to-end delay bounds. Its popular subbranch using moment-generating function bounds allows for accurate bounds under the assumption of independence. However, in the dependent flow case, standard techniques typically invoke Hölder's inequality, which in many cases leads to loose bounds. Furthermore, optimization of the Hölder parameters is computationally expensive. In this work, we show that two simple, yet effective techniques related to the deterministic network calculus are able to improve the delay analysis in many scenarios, while at the same time enabling a considerably faster computation. Specifically, in a thorough numerical evaluation of two case studies, we show that using the proposed techniques: 1. we can improve the stochastic delay bounds often considerably and sometimes even obtain a bound where the standard technique provides no finite bound; 2. computation times are decreased by about two orders of magnitude.

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Cited By

Bouillard A(2024)MGF-based SNC for stationary independent Markovian processes with localized application of martingalesDiscrete Event Dynamic Systems10.1007/s10626-024-00399-x34:2(375-401)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s10626-024-00399-x
Yang QPeng XYang HZhang GBai B(2023)dMAPAR-HMM: Reforming Traffic Model for Improving Performance Bound with Stochastic Network Calculus2023 7th Network Traffic Measurement and Analysis Conference (TMA)10.23919/TMA58422.2023.10199100(1-9)Online publication date: 26-Jun-2023
https://doi.org/10.23919/TMA58422.2023.10199100
Geyer FScheffler ABondorf S(2023)Network Calculus With Flow Prolongation – A Feedforward FIFO Analysis Enabled by MLIEEE Transactions on Computers10.1109/TC.2022.320422572:1(97-110)Online publication date: 1-Jan-2023
https://doi.org/10.1109/TC.2022.3204225
Show More Cited By

Index Terms

Improving Delay Bounds in the Stochastic Network Calculus by Using less Stochastic Inequalities
1. Networks
  1. Network performance evaluation
    1. Network performance analysis
    2. Network performance modeling

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VALUETOOLS '20: Proceedings of the 13th EAI International Conference on Performance Evaluation Methodologies and Tools

May 2020

217 pages

ISBN:9781450376464

DOI:10.1145/3388831

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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EAI: The European Alliance for Innovation

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New York, NY, United States

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Published: 29 May 2020

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VALUETOOLS '20

VALUETOOLS '20: 13th EAI International Conference on Performance Evaluation Methodologies and Tools

May 18 - 20, 2020

Tsukuba, Japan

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Cited By

Bouillard A(2024)MGF-based SNC for stationary independent Markovian processes with localized application of martingalesDiscrete Event Dynamic Systems10.1007/s10626-024-00399-x34:2(375-401)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s10626-024-00399-x
Yang QPeng XYang HZhang GBai B(2023)dMAPAR-HMM: Reforming Traffic Model for Improving Performance Bound with Stochastic Network Calculus2023 7th Network Traffic Measurement and Analysis Conference (TMA)10.23919/TMA58422.2023.10199100(1-9)Online publication date: 26-Jun-2023
https://doi.org/10.23919/TMA58422.2023.10199100
Geyer FScheffler ABondorf S(2023)Network Calculus With Flow Prolongation – A Feedforward FIFO Analysis Enabled by MLIEEE Transactions on Computers10.1109/TC.2022.320422572:1(97-110)Online publication date: 1-Jan-2023
https://doi.org/10.1109/TC.2022.3204225
Bouillard ANikolaus PSchmitt J(2022)Unleashing the Power of Paying Multiplexing Only Once in Stochastic Network CalculusProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/35308976:2(1-27)Online publication date: 6-Jun-2022
https://dl.acm.org/doi/10.1145/3530897
Li XXu XChen LZhang HJu ZGuo W(2022)Longitudinal speed tracking control for an electric connected vehicle with actuator saturation subject to a replay attackNonlinear Dynamics10.1007/s11071-022-07898-2111:2(1369-1383)Online publication date: 28-Oct-2022
https://doi.org/10.1007/s11071-022-07898-2

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