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Jozef Sabo

dblp:331/3980 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none

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

Computer networks · 1 · 1 since 2021

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
Internet architecture and protocols · 38% Network measurement and analytics · 38% Routing and switching · 23%

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

TopicWeightPapersLastEvidence papers
Internet architecture and protocols › packet processing
packet classification
0.612022
ClassBench-ng: Benchmarking Packet Classification Algorithms in the OpenFlow Era · IEEE/ACM Trans. Netw. 2022
Network measurement and analytics
workload characterization
0.612022
ClassBench-ng: Benchmarking Packet Classification Algorithms in the OpenFlow Era · IEEE/ACM Trans. Netw. 2022
Routing and switching
IP lookup
0.212022
ClassBench-ng: Benchmarking Packet Classification Algorithms in the OpenFlow Era · IEEE/ACM Trans. Netw. 2022
Routing and switching
packet forwarding
0.212022
ClassBench-ng: Benchmarking Packet Classification Algorithms in the OpenFlow Era · IEEE/ACM Trans. Netw. 2022

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

benchmarking · 0.6
YearPublicationVenuePosition
2022 ClassBench-ng: Benchmarking Packet Classification Algorithms in the OpenFlow Era
abstract
Packet classification, i.e., the process of categorizing packets into flows, is a first-class citizen in any networking device. Every time a new packet has to be processed, one or more header fields need to be compared against a set of pre-installed rules. This is done for basic forwarding operations, to apply security policies, application-specific processing, or quality-of-service guarantees. A lot of research efforts have identified better lookup techniques, i.e., finding the best match between packet headers and rules, by capitalizing on the rule sets characteristics. Here, ClassBench has greatly served the community by enabling the generation of IPv4 rule sets. In this paper, we present a new tool, ClassBench-ng, that creates synthetic IPv4, IPv6, and OpenFlow rules. We start from an analysis of classification rules deployed in-the-wild and we use the findings to craft our solution. ClassBench-ng can generate a user-defined number of rules as well as an associated header trace matching them. Compared to state-of-the-art solutions, the rule set generation process is usually more accurate and it is able to produce rules matching a number of different use cases, i.e., from an IPv4 router to an OpenFlow switch, which is unique among current rule set generation tools.
Jirí Matousek 0002, Adam Lucanský, David Janecek, Jozef Sabo, Jan Korenek, Gianni Antichi
IEEE/ACM Trans. Netw.4