Abstract
Worst-case bounds on flow delays are essential for safety-critical systems. Deterministic network calculus is a methodology to compute such bounds. It is actively researched regarding its modeling capabilities as well as analysis accuracy and performance. We provide a contribution to the major part of the analysis: bounding the arrivals of cross flows. In particular, it has been believed that an aggregate view on cross flows outperforms deriving a bound for each cross flow individually. In contrast, we show that the so-called cross-flow segregation, can outperform the aggregation approach under certain conditions. We give a proof of concept, combine the alternative approaches into an analysis computing best bounds, and evaluate accuracy improvements as well as computational effort increases. To that end, we show that flows known to suffer from overly pessimistic delay bounds can see this pessimism reduced by double-digit percentages.
This work has been conducted at the Distributed Computer Systems (DISCO) Lab, TU Kaiserslautern, Germany, with support of a Carl Zeiss Foundation grant.
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Bondorf, S., Nikolaus, P., Schmitt, J.B. (2018). Catching Corner Cases in Network Calculus – Flow Segregation Can Improve Accuracy. In: German, R., Hielscher, KS., Krieger, U. (eds) Measurement, Modelling and Evaluation of Computing Systems. MMB 2018. Lecture Notes in Computer Science(), vol 10740. Springer, Cham. https://doi.org/10.1007/978-3-319-74947-1_15
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DOI: https://doi.org/10.1007/978-3-319-74947-1_15
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