Evaluation of end-to-end network performance using realistic traffic models is a challenging problem in networking. The classical theory of queueing networks is feasible only under rather restrictive assumptions on the input traffic models and network elements. An alternative approach, first proposed in the late 1980s, is to impose deterministic bounds on the input traffic that can be used as a basis for a network calculus to compute end-to-end network delay bounds. Such deterministic bounds are inherently loose as they must accommodate worst case scenarios. Since the early 1990s, efforts have shifted to development of a stochastic network calculus to provide probabilistic end-to-end performance bounds. In this paper, we capitalize on the approach of stochastically bounded burstiness (SBB) which was developed for a general class of bounding functions, and was demonstrated for a bound that is based on a mixture distribution. We specialize the SBB bounds to bounds based on the class of phase-type distributions, which includes mixture distributions as a particular case. We develop the phase-type bounds and demonstrate their performance.
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Bounding network-induced delays for time-critical services in avionic systems using measurements and network calculus
In this work, we propose to derive realistic, accurate bounds on network-induced delays for time-critical tasks running on Avionics Full-Duplex Switched Ethernet. In the WiP poster, we present preliminary evaluation results showing that through measurement-based modeling and refining network-calculus-based analysis with measurements, tight delay bounds can be obtained for AFDX networks with realistic traffic patterns and network workloads.
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- NSF-PAR ID:
- 10120465
- Date Published:
- Journal Name:
- Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS '19)
- Page Range / eLocation ID:
- 338 to 339
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3302509.3313330
