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Roberto Ferretti

dblp:19/3586 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2012
—ORCID · unresolved

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

Artificial intelligence and machine learning · 1Computer networks · 1Graphics, computer vision, multimedia, augmented reality and games · 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 architecture, parallel and distributed computing, and storage systems
1 paper
Performance modeling and evaluation · 70% Interconnection networks and networks-on-chip · 30%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Interconnection networks and networks-on-chip › switch architecture
buffer design		 0.011991
ATM System Buffer Design Under Very Low Cell Loss Probability Constraints · INFOCOM 1991
Performance modeling and evaluation › queueing models
cell loss probability		 0.011991
ATM System Buffer Design Under Very Low Cell Loss Probability Constraints · INFOCOM 1991
Performance modeling and evaluation
queueing models		 0.011991
ATM System Buffer Design Under Very Low Cell Loss Probability Constraints · INFOCOM 1991
Performance modeling and evaluation
simulation		 0.011991
ATM System Buffer Design Under Very Low Cell Loss Probability Constraints · INFOCOM 1991

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

monte carlo simulation · 0.0generalized extreme value theory · 0.0
YearPublicationVenuePosition
2012 A Semi-Lagrangian scheme for area preserving flows
Elisabetta Carlini, Roberto Ferretti
 ICPR2
1991 ATM System Buffer Design Under Very Low Cell Loss Probability Constraints
abstract
This work deals with a general method for buffer design in an ATM system in which the target loss probability should be very low (e.g. less than 10/sup -9/). This method is based on the application of the generalized extreme value theory (GEVT) to results arising from simulation runs. This theory allows the estimation of very small probabilities which would not be evaluable with the traditional Monte Carlo approach. An advantage of 4/5 decades with respect to Monte Carlo limits can be obtained utilizing the same sample set. The extension of the theory to the case of distribution functions of discrete random variables is discussed. The applicability of the method is demonstrated with reference to known probability distribution functions. Moreover, the GEVT is applied in the cases of classical queuing systems. The general criteria for the evaluation of the basic GEVT parameters are discussed.>
Francesco Bernabei, Roberto Ferretti, Marco Listanti, Giuseppe Zingrillo
 INFOCOM2