Packet Classification Using Self-Organized Map
Marco Montagna
Packet Classification Using Self-Organized Map.
Rel. Mariagrazia Graziano. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Elettronica (Electronic Engineering), 2019
|
Preview |
PDF (Tesi_di_laurea)
- Tesi
Licenza: Creative Commons Attribution Non-commercial No Derivatives. Download (1MB) | Preview |
Abstract
The main goal of this thesis titled "Packet Classification using Growing Hierarchical Self-Organizing Map" is to validate the possibility of using a new approach for a well know problem. The core idea is to present a possible implementation of a Growing Hierarchical Self-Organized Map (GH-SOM) for use in packet classification in the context of software-defined networking (SDN). SDN applications are characterized by frequent network configuration updates on-the-fly and a large number of rules thereby requiring a highly flexible packet classification mechanism. Moreover, as network bandwidths continue to grow, a high-performance packet classification is imperative.\par Today's technology adopted for packet classification does not scale well in throughput and power consumption as number of rules increase.
The main change proposed in this implementation is to move all the complexity to an offline phase, train a modeled neural network and exploit its properties to provide a more efficient solution for packet classification during run time
Relatori
Tipo di pubblicazione
URI
![]() |
Modifica (riservato agli operatori) |
