In band Network Telemetry (INT) is a technique aiming at collecting telemetry information by inserting it inside the data packets, instead of relying on classical centralized monitoring elements that periodically query the network devices. The main drawback of INT is represented by the introduced perpacket overhead, that could negatively affect some traffic flows, especially those having stringent QoS requirements. To deal with the increase in the packet length caused by INT, in this paper we introduce the Sampling and Recovering paradigm to overcome the classical Collect Everything approach where all the INT data must be gathered. The proposed approach hinges on signal processing strategies to sample and recover sparse flow signals. The key idea is to reduce the number of INT data to collect and exploit signal reconstruction algorithms to obtain the unseen samples. The preliminary performance evaluation shows that the 18% of INT data are enough to get an accurate reconstruction of the overall network situation, while allowing for 90% of overhead reduction with respect to the Collect Everything case.
In Band Network Telemetry Overhead Reduction Based on Data Flows Sampling and Recovering / Sardellitti, S; Polverini, M; Barbarossa, S; Cianfrani, A; Di Lorenzo, P; Listanti, M. - (2023), pp. 414-419. (Intervento presentato al convegno IEEE NetSoft 2023 tenutosi a Madrid, Spain) [10.1109/NetSoft57336.2023.10175471].
In Band Network Telemetry Overhead Reduction Based on Data Flows Sampling and Recovering
Sardellitti, S;Polverini, M;Barbarossa, S;Cianfrani, A;Di Lorenzo, P;Listanti, M
2023
Abstract
In band Network Telemetry (INT) is a technique aiming at collecting telemetry information by inserting it inside the data packets, instead of relying on classical centralized monitoring elements that periodically query the network devices. The main drawback of INT is represented by the introduced perpacket overhead, that could negatively affect some traffic flows, especially those having stringent QoS requirements. To deal with the increase in the packet length caused by INT, in this paper we introduce the Sampling and Recovering paradigm to overcome the classical Collect Everything approach where all the INT data must be gathered. The proposed approach hinges on signal processing strategies to sample and recover sparse flow signals. The key idea is to reduce the number of INT data to collect and exploit signal reconstruction algorithms to obtain the unseen samples. The preliminary performance evaluation shows that the 18% of INT data are enough to get an accurate reconstruction of the overall network situation, while allowing for 90% of overhead reduction with respect to the Collect Everything case.File | Dimensione | Formato | |
---|---|---|---|
Sardellitti_postprint_In-Band_2023.pdf.pdf
embargo fino al 01/08/2025
Tipologia:
Documento in Post-print (versione successiva alla peer review e accettata per la pubblicazione)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
445.94 kB
Formato
Adobe PDF
|
445.94 kB | Adobe PDF | Contatta l'autore |
Sardellitti_In-Band_2023.pdf
solo gestori archivio
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
525.62 kB
Formato
Adobe PDF
|
525.62 kB | Adobe PDF | Contatta l'autore |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.