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Ariel Shaqed

dblp:133/3832 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2013
—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 performance modeling · 50% Routing and switching · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Network performance modeling
network utilization
0.212013
Network utilization: The flow view · INFOCOM 2013
Routing and switching
traffic engineering
0.212013
Network utilization: The flow view · INFOCOM 2013

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

flow-level analysis · 0.2
YearPublicationVenuePosition
2013 Network utilization: The flow view
abstract
Building and operating a large backbone network can take months or even years, and it requires a substantial investment. Therefore, there is an economical drive to increase the utilization of network resources (links, switches, etc.) in order to improve the cost efficiency of the network. At the same time, the utilization of network components has a direct impact on the performance of the network and its resilience to failure, and thus operational considerations are a critical aspect of the decision regarding the desired network load and utilization. However, the actual utilization of the network resources is not easy to predict or control. It depends on many parameters like the traffic demand and the routing scheme (or Traffic Engineering if deployed), and it varies over time and space. As a result it is very difficult to actually define real network utilization and to understand the reasons for this utilization. In this paper we introduce a novel way to look at the network utilization. Unlike traditional approaches that consider the average link utilization, we take the flow perspective and consider the network utilization in terms of the growth potential of the flows in the network. After defining this new Flow Utilization, and discussing how it differs from common definitions of network utilization, we study ways to efficiently compute it over large networks. We then show, using real backbone data, that Flow Utilization is very useful in identifying network state and evaluating performance of TE algorithms.
Avinatan Hassidim, Danny Raz, Michal Segalov, Ariel Shaqed
INFOCOM4