A lightweight, scalable and distributed admission control algorithm for voice traffic

Kulkarni, P., Dinit, P., McClean, S., Parr, G. ORCID: https://orcid.org/0000-0002-9365-9132 and Black, M. (2007) A lightweight, scalable and distributed admission control algorithm for voice traffic. In: IEEE International Conference on Communications. The Institute of Electrical and Electronics Engineers (IEEE), GBR, pp. 556-561. ISBN 1-4244-0353-7

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Abstract

The idea of carrying voice traffic on IP networks has been found to be very lucrative. However to achieve a quality similar to that offered by the existing telephone networks, the IP network should be in a position to honour the stringent quality of service (QoS) requirements of voice traffic. If traffic in excess of the network capacity is admitted into the network, QoS may be violated resulting in performance degradation. One way of providing sustained and consistent QoS is by regulating the number of voice calls that are admitted into the network such that the load on the network is less than or equal to the capacity of the network. This mechanism is known as call admission control. This paper proposes a scalable and distributed call admission control algorithm that operates at the network edge and factors the local passive measurements into the admission decision. Performance evaluation of the proposed algorithm through ns2 simulations reveals that it is successful in detecting rate mismatches (input rate greater than output rate) and subsequently rejecting admission requests (as long as input rate is greater than output rate) thereby delivering on the QoS guarantees demanded by voice applications. Moreover, it is simple and lightweight from an implementation perspective.

Item Type:	Book Section
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Smart Emerging Technologies Faculty of Science > Research Groups > Cyber Security Privacy and Trust Laboratory Faculty of Science > Research Groups > Data Science and AI
Depositing User:	Pure Connector
Date Deposited:	24 Sep 2016 01:07
Last Modified:	10 Dec 2024 01:10
URI:	https://ueaeprints.uea.ac.uk/id/eprint/60538
DOI:	10.1109/ICC.2007.97

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