Reference : Multi-Gbps HTTP traffic analysis in commodity hardware based on local knowledge of TC...

	Document type :	Scientific journals : Article
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/37222

Title :	Multi-Gbps HTTP traffic analysis in commodity hardware based on local knowledge of TCP streams
Language :	-
Author, co-author :	Vega Moreno, Carlos Gonzalo [Universidad Autónoma de Madrid > Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones]
	Roquero, Paula [Universidad Autónoma de Madrid > Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones]
	Aracil, Javier [Universidad Autónoma de Madrid > Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones]
Publication date :	2017
Journal title :	Computer Networks
Publisher :	Elsevier B.V.
Volume :	113
Pages :	258-268
Peer reviewed :	Yes (verified by ORBilu)
Audience :	International
ISSN :	13891286
Keywords :	[en] High speed analysis ; HTTP ; Traffic analysis
Abstract :	[en] In this paper we propose and implement novel techniques for performance evaluation of web traffic (response time, response code, etc.), with no reassembly of the underlying TCP connection, which severely restricts the traffic analysis throughput. Furthermore, our proposed software for HTTP traffic analysis runs in standard hardware, which is very cost-effective. Besides, we present sub-TCP connection load balancing techniques that significantly increase throughput at the expense of losing very few HTTP transactions. Such techniques provide performance evaluation statistics which are indistinguishable from the single-threaded alternative with full TCP connection reassembly. © 2017 Elsevier B.V.
Permalink :	http://hdl.handle.net/10993/37222
DOI :	10.1016/j.comnet.2017.01.001
Other URL :	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009104902&doi=10.1016%2fj.comnet.2017.01.001&partnerID=40&md5=3c957f070084fb80780524c49414df90
Framework Programme / European Project :	FP7 ; 318389 - FED4FIRE - Federation for FIRE

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