Measurement-based traffic classification in differentiated services

Marko Luoma; Mika Ilvesmaeki

doi:10.1117/12.434337

24 July 2001 Measurement-based traffic classification in differentiated services

Marko Luoma, Mika Ilvesmaeki

Proceedings Volume 4523, Internet Performance and Control of Network Systems II; (2001) https://doi.org/10.1117/12.434337
Event: ITCom 2001: International Symposium on the Convergence of IT and Communications, 2001, Denver, CO, United States

Abstract

Internet is moving towards the time of Quality of Service (QoS) networking. This move is taking place through the application of Differentiated Services (DiffServ) architecture. DiffServ offers low overhead tools to implement class based differentiation for the traffic. Decision of differentiation is, however, left as an open matter, to be settled between service provider and customer. Majority of customers are, based on our assumption, not ready to say what should be the quality or class for their traffic. This leaves space for provider intervention - service, to do this classification for the customer. Service provider is dealing with three problems which need to be solved concurrently: (1)Deciding the proper forwarding class for the application data stream (2)Separation of application flows from the packet stream (3)Constructing proper forwarding treatments. If successful with this operation, operator has direct control over the resource utilization within different classes and therefore service level provided to the customer. In order to cope with this service, tools for analyzing network traffic and forming suitable traffic groups are required. We present algorithms and methodologies which do differentiation of traffic based on the activity/traffic characteristics of applications. These values are determined from the flow analysis of packet lengths and inter-sending times.

Citation Download Citation

Marko Luoma and Mika Ilvesmaeki "Measurement-based traffic classification in differentiated services", Proc. SPIE 4523, Internet Performance and Control of Network Systems II, (24 July 2001); https://doi.org/10.1117/12.434337

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Internet

Atrial fibrillation

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Data analysis

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