Application of artificial intelligence to improve quality of service in computer networks |
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Authors: | Iftekhar Ahmad Joarder Kamruzzaman Daryoush Habibi |
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Affiliation: | (1) School of Engineering, Edith Cowan University, Joondalup, WA, Australia;(2) School of Computing and Information Technology, Monash University, Melbourne, VIC, Australia |
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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. |
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