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Cited by 1,016

The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.

TCP and explicit congestion notification

Open access
Floyd, Sally·1994·ACM SIGCOMM Computer Communication Review

Abstract

This paper discusses the use of Explicit Congestion Notification (ECN) mechanisms in the TCP/IP protocol. The first part proposes new guidelines for TCP's response to ECN mechanisms (e.g., Source Quench packets, ECN fields in packet headers). Next, using simulations, we explore the benefits and drawbacks of ECN in TCP/IP networks. Our simulations use RED gateways modified to set an ECN bit in the IP packet header as an indication of congestion, with Reno-style TCP modified to respond to ECN as well as to packet drops as indications of congestion. The simulations show that one advantage of ECN mechanisms is in avoiding unnecessary packet drops, and therefore avoiding unnecessary delay for packets from low-bandwidth delay-sensitive TCP connections. A second advantage of ECN mechanisms is in networks (generally LANs) where the effectiveness of TCP retransmit timers is limited by the coarse granularity of the TCP clock. The paper also discusses some implementation issues concerning specific ECN mechanisms in TCP/IP networks.

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Metadata

Authors
Floyd, S.
Venue
ACM SIGCOMM Computer Communication Review
Published
1994-10-20
Volume
24
Issue
5
Pages
8-23
ISSN
0146-4833 (ISSN-L)
OpenAlex
W1970131865
Cited by
1,016
References
13
FWCI
10.43
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: TCP and explicit congestion notification. https://app.cochat.ai/papers/10.1145/205511.205512
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Floyd, S. (1994). TCP and explicit congestion notification. ACM SIGCOMM Computer Communication Review. https://doi.org/10.1145/205511.205512

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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