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Shawn A. Routhier

dblp:167/6541 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1986
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Network measurement and analytics · 46% Network performance modeling · 46% Internet architecture and protocols · 7%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Network performance modeling › traffic modeling
packet train model
0.011986
Packet Trains-Measurements and a New Model for Computer Network Traffic · IEEE J. Sel. Areas Commun. 1986
Network measurement and analytics
traffic characterization
0.011986
Packet Trains-Measurements and a New Model for Computer Network Traffic · IEEE J. Sel. Areas Commun. 1986
Network measurement and analytics
traffic measurement
0.011986
Packet Trains-Measurements and a New Model for Computer Network Traffic · IEEE J. Sel. Areas Commun. 1986
Network performance modeling
traffic modeling
0.011986
Packet Trains-Measurements and a New Model for Computer Network Traffic · IEEE J. Sel. Areas Commun. 1986
Internet architecture and protocols
local area network
0.011986
Packet Trains-Measurements and a New Model for Computer Network Traffic · IEEE J. Sel. Areas Commun. 1986

Methods — techniques the papers use, named apart from their topics

statistical analysis · 0.0arrival process modeling · 0.0
YearPublicationVenuePosition
1986 Packet Trains-Measurements and a New Model for Computer Network Traffic
abstract
Traffic measurements on a ring local area computer network at the Massachusetts Institute of Technology are presented. The analysis of the arrival pattern shows that the arrival processes are neither Poisson nor compound Poisson. An alternative model called "packet train" is proposed. In the train model, the traffic on the network consists of a number of packet streams between various pairs of nodes on the network. Each node-pair stream (or node-pair process, as we call them) consists of a number of trains. Each train consists of a number of packets (or cars) going in either direction (from node A to B or from node B to A). The intercar gap is large (compared to packet transmission time) and random. The intertrain time is even larger. The Poisson and the compound Poisson arrivals are shown to be special cases of the train arrival model. Another important observation is that the packet arrivals exhibit a "source locality." If a packet is seen on the network going from A to B, the probability of the next packet going from A to B or from B to A is very high. Implications of the train arrivals and of source locality on the design of bridges, gateways, and reservation protocols are discussed. A numbet of open problems requiring development of analysis techniques for systems with train arrival processes are also described.
Raj Jain, Shawn A. Routhier
IEEE J. Sel. Areas Commun.2