Title

Application of artificial intelligence to improve quality of service in computer networks

Document Type

Journal Article

Publisher

Springer-Verlag London Ltd

Faculty

Faculty of Computing, Health and Science

School

School of Engineering (SOE) / Centre for Communications Engineering Research

RAS ID

12564

Comments

This article was originally published as: Ahmad, I. , Kamruzzaman, J., & Habibi, D. (2011). Application of artificial intelligence to improve quality of service in computer networks. Neural Computing and Applications, 21(1), 81-90. Original article available here

Abstract

Resource sharing between book-ahead (BA) and instantaneous request (IR) reservation often results in high preemption rates for ongoing IR calls in computer networks. High IR call preemption rates cause interruptions to service continuity, which is considered detrimental in a QoS-enabled network. A number of call admission control models have been proposed in the literature to reduce preemption rates for ongoing IR calls. Many of these models use a tuning parameter to achieve certain level of preemption rate. This paper presents an artificial neural network (ANN) model to dynamically control the preemption rate of ongoing calls in a QoS-enabled network. The model maps network traffic parameters and desired operating preemption rate by network operator providing the best for the network under consideration into appropriate tuning parameter. Once trained, this model can be used to automatically estimate the tuning parameter value necessary to achieve the desired operating preemption rates. Simulation results show that the preemption rate attained by the model closely matches with the target rate.

DOI

10.1007/s00521-011-0622-6

 

Link to publisher version (DOI)

10.1007/s00521-011-0622-6