Improving the real-time behavior of ethernet networks using traffic smoothing

In modern process control systems, Ethernet is achieving a leading position, proposing itself as a network capable of supporting all communication needs at all levels in the Computer Integrated Manufacturing hierarchy. The main obstacle to using Ethernet at the Field level is the nondeterminism of t...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2005-08, Vol.1 (3), p.151-161
Main Authors: Lo Bello, L., Kaczynski, G.A., Mirabella, O.
Format: Article
Language:English
Subjects:
Access protocols
Communication system traffic control
Computer integrated manufacturing
Ethernet
Ethernet networks
fuzzy control
Genetic algorithms
Industrial control
Manufacturing industries
Media Access Protocol
Process control
Process controls
real-time communication
Smoothing methods
Studies
Telecommunication traffic
traffic smoothing
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Summary:In modern process control systems, Ethernet is achieving a leading position, proposing itself as a network capable of supporting all communication needs at all levels in the Computer Integrated Manufacturing hierarchy. The main obstacle to using Ethernet at the Field level is the nondeterminism of the Ethernet MAC protocol, which cannot provide real-time traffic with bounded channel access times. This paper focuses on industrial applications featuring soft real-time constraints, such as periodic control or industrial multimedia, which do not require deterministic guarantees on deadline meeting. To cope with this class of applications, Ethernet should be able to guarantee the timely delivery of real-time packets in statistical terms. The paper presents fuzzy traffic smoothing, a technique to perform adaptive traffic smoothing over Ethernet networks at the Field level thus enabling them to provide a statistical bound on packet delivery time. Previous work showed that the fuzzy smoother outperforms other adaptive smoothers proposed in the literature. This paper addresses fuzzy smoother optimization through genetic algorithms. The proposed optimization is applied to tune the inference engine membership functions. The results obtained show the effectiveness of the approach.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2005.852071