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A reliable multicast framework for light-weight sessions and application level framing

Floyd, S., Jacobson, V., Liu, C.-G., McCanne, S., Zhang, L.·1997·IEEE/ACM Transactions on Networking

Abstract

This paper describes scalable reliable multicast (SRM), a reliable multicast framework for light-weight sessions and application level framing. The algorithms of this framework are efficient, robust, and scale well to both very large networks and very large sessions. The SRM framework has been prototyped in wb, a distributed whiteboard application, which has been used on a global scale with sessions ranging from a few to a few hundred participants. The paper describes the principles that have guided the SRM design, including the IP multicast group delivery model, an end-to-end, receiver-based model of reliability, and the application level framing protocol model. As with unicast communications, the performance of a reliable multicast delivery algorithm depends on the underlying topology and operational environment. We investigate that dependence via analysis and simulation, and demonstrate an adaptive algorithm that uses the results of previous loss recovery events to adapt the control parameters used for future loss recovery. With the adaptive algorithm, our reliable multicast delivery algorithm provides good performance over a wide range of underlying topologies.

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Metadata

Authors
Floyd, S., Jacobson, V., Liu, C.-G., McCanne, S., & Zhang, L.
Venue
IEEE/ACM Transactions on Networking
Published
1997
Volume
5
Issue
6
Pages
784-803
ISSN
1063-6692 (ISSN-L)
OpenAlex
W2169600013
Cited by
1,168
References
0
FWCI
130.05
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A reliable multicast framework for light-weight sessions and application level framing. https://app.cochat.ai/papers/10.1109/90.650139
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Floyd, S., Jacobson, V., Liu, C.-G., McCanne, S., & Zhang, L. (1997). A reliable multicast framework for light-weight sessions and application level framing. IEEE/ACM Transactions on Networking. https://doi.org/10.1109/90.650139

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.