Abstract
Guaranteeing performance bounds of data flows is an essential part of network engineering and certification of networks with real-time constraints. A prevalent analytical method to derive guarantees for end-to-end delay and buffer size is Deterministic Network Calculus (DNC). Due to the DNC system model, one decisive restriction is that only unicast flows can be analyzed. Previous attempts to analyze networks with multicast flows circumvented this restriction instead of overcoming it. E.g., they replaced the system model with an overly-pessimistic one that consists of unicast flows only. Such approaches impair modeling accuracy and thus inevitably result in inaccurate performance bounds.
In this chapter, we approach the problem of multicast flows differently. We start from the existing DNC analysis procedure, the unicast feed-forward analysis, and generalize it to a multicast feed-forward analysis. To that end, we contribute a novel analysis procedure that leaves the network model containing multicast flows unchanged, preserves its accuracy, allows for DNC principles such as pay multiplexing only once, and therefore derives more accurate performance bounds than existing approaches.
This chapter is an extended version of [4]: Generalizing Network Calculus Analysis to Derive Performance Guarantees for Multicast Flows, in Proc. of EAI ValueTools (2016).
Part of this work has been conducted at the Distributed Computer Systems Lab, TU Kaiserslautern (TUK), D-67663 Kaiserslautern, Germany, with support of the Carl Zeiss Foundation.
Part of this work has been conducted at Airbus Group Innovations.
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- 1.
Due to a lack of software tools, TA and FA are not included.
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Acknowledgements
The authors would like to thank Bruno Oliveira Cattelan for his work on implementing the explicit intermediate bounds analysis and the multicast feed-forward analysis in the Disco Deterministic Network Calculator.
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Bondorf, S., Geyer, F. (2019). Deterministic Network Calculus Analysis of Multicast Flows. In: Puliafito, A., Trivedi, K. (eds) Systems Modeling: Methodologies and Tools. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-92378-9_5
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