Packet Loss Measurement Based on Sampled Flow
This paper is devoted to further strengthening, in the current asymmetric information environment, the informed level of operators about network performance. Specifically, in view of the burst and perishability of a packet loss event, to better meet the real-time requirements of current high-speed b...
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
MDPI AG
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/1867bd7ca7c54167b83c9143fd01af74 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | This paper is devoted to further strengthening, in the current asymmetric information environment, the informed level of operators about network performance. Specifically, in view of the burst and perishability of a packet loss event, to better meet the real-time requirements of current high-speed backbone performance monitoring, a model for <i>P</i>acket <i>L</i>oss <i>M</i>easurement at the access network boundary <i>B</i>ased on <i>S</i>ampled <i>F</i>low (<i>PLMBSF</i>) is presented in this paper under the premise of both cost and real-time. The model overcomes problems such as the inability of previous estimation to distinguish between packet losses before and after the monitoring point, deployment difficulties and cooperative operation consistency. Drawing support from the Mathis equation and regression analysis, the measurement for packet losses before and after the monitoring point can be realized when using only the sampled flows generated by the access network boundary equipment. The comparison results with the trace-based passive packet loss measurement show that although the proposed model is easily affected by factors such as flow length, loss rate, sampling rate, the overall accuracy is still within the acceptable range. In addition, the proposed model <i>PLMBSF</i>, compared with the trace-based loss measurement is only different in the input data granularity. Therefore, <i>PLMBSF</i> and its advantages are also applicable to aggregated traffic. |
|---|