Class-of-service mapping for QoS: A statistical signature-based approach to IP traffic classification (2004)
| Venue: | In IMC’04 |
| Citations: | 61 - 3 self |
BibTeX
@INPROCEEDINGS{Roughan04class-of-servicemapping,
author = {Matthew Roughan and Subhabrata Sen and Oliver Spatscheck and Nick Duffield},
title = {Class-of-service mapping for QoS: A statistical signature-based approach to IP traffic classification},
booktitle = {In IMC’04},
year = {2004},
pages = {135--148}
}
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Abstract
The ability to provide different Quality of Service (QoS) guarantees to traffic from different applications is a highly desired feature for many IP network operators, particularly for enterprise networks. Although various mechanisms exist for providing QoS in the network, QoS is yet to be widely deployed. We believe that a key factor holding back widespread QoS adoption is the absence of suitable methodologies/processes for appropriately mapping the traffic from different applications to different QoS classes. This is a challenging task, because many enterprise network operators who are interested in QoS do not know all the applications running on their network, and furthermore, over recent years port-based application classification has become problematic. We argue that measurement based automated Class of Service (CoS) mapping is an important practical problem that needs to be studied. In this paper we describe the requirements and associated challenges, and outline a solution framework for measurement based classification of traffic for QoS based on statistical application signatures. In our approach the signatures are chosen in such as way as to make them insensitive to the particular application layer protocol, but rather to determine the way in which an application is used – for instance is it used interactively, or for bulk-data transport. The resulting application signature can then be used to derive the network layer signatures required to determine the CoS class for individual IP datagrams. Our evaluations using traffic traces from a variety of network locations, demonstrate the feasibility and potential of the approach.
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