Abstract

In network measurement systems, packet sampling techniques are usually adopted to reduce the overall amount of data to collect and process. Being based on a subset of packets, they introduce estimation errors that have to be properly counteracted by using a fine tuning of the sampling strategy and sophisticated inversion methods. This problem has been deeply investigated in the literature with particular attention to the statistical properties of packet sampling and to the recovery of the original network measurements. Herein, we propose a novel approach to predict the energy of the sampling error in the real time estimation of traffic bitrate, based on spectral analysis in the frequency domain. We start by demonstrating that the error introduced by packet sampling can be modeled as an aliasing effect in the frequency domain. Then, we derive closed-form expressions for the Signal-to-Noise Ratio (SNR) to predict the distortion of traffic bitrate estimates over time. The accuracy of the proposed SNR metric is validated by means of real packet traces. Furthermore, a comparison with respect to an analogous SNR expression derived using classic stochastic tools is proposed, showing that the frequency domain approach grants for a higher accuracy when traffic rate measurements are carried out at fine time granularity.

Autore Pugliese

• Grieco Luigi Alfredo

Tutti gli autori

• GRIECO L , C. BARAKAT , M. MARZULLI

Titolo volume/Rivista

IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT

Anno di pubblicazione

2011

ISSN

1932-4537

ISBN

Non Disponibile

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

2

Ultimo Aggiornamento Citazioni

2017-04-23 03:20:56

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile