Online detection of pathological TCP flows with retransmissions in high-speed networks
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Fecha
2018Autor
Versión
Acceso abierto / Sarbide irekia
Tipo
Artículo / Artikulua
Versión
Versión aceptada / Onetsi den bertsioa
Impacto
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10.1016/j.comcom.2018.06.002
Resumen
Online Quality of Service (QoS) assessment in high speed networks is one of the key concerns for service providers, namely to detect QoS degradation on-the-fly as soon as possible and avoid customers’ complaints. In this regard, a Key Performance Indicator (KPI) is the number of TCP retransmissions per flow, which is related to packet losses or increased network and/or client/server latency. Howe ...
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Online Quality of Service (QoS) assessment in high speed networks is one of the key concerns for service providers, namely to detect QoS degradation on-the-fly as soon as possible and avoid customers’ complaints. In this regard, a Key Performance Indicator (KPI) is the number of TCP retransmissions per flow, which is related to packet losses or increased network and/or client/server latency. However, to accurately detect TCP retransmissions the whole sequence number list should be tracked which is a challenging task in multi-Gb/s networks.
In this paper we show that the simplest approach of counting as a retransmission a packet whose sequence number is smaller than the previous one is enough to detect pathological flows with severe retransmissions. Such a lightweight approach eliminates the need of tracking the whole TCP flow history, which severely restricts traffic analysis throughput. Our findings show that low False Positive Rates (FPR) and False Negative Rates (FNR) can be achieved in the detection of such pathological flows with severe retransmissions, which are of paramount importance for QoS monitoring. Most importantly, we show that live detection of such pathological flows at 10 Gb/s rate per processing core is feasible. [--]
Materias
Network management,
Performance monitoring,
Quality of service,
TCP retransmissions,
TCP modeling
Editor
Elsevier
Publicado en
Computer Communications, vol. 127, september 2018, pp. 95-104
Departamento
Universidad Pública de Navarra. Departamento de Automática y Computación /
Nafarroako Unibertsitate Publikoa. Automatika eta Konputazioa Saila
Versión del editor
Entidades Financiadoras
This work has been partially funded by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund under the projects TRÁFICA (MINECO / FEDER TEC2015-69417-C2-1-R), Preproceso Inteligente de Tráfico (MINECO / FEDER TEC2015-69417-C2-2-R) and RACING DRONES (MINECO / FEDER RTC-2016-4744-7). The authors also thank the Spanish Ministry of Education, Culture and Sport for a collaboration grant.