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

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

Application of Artificial Intelligence in Computer Network Technology Under the Background of Big Data

“…Application of artificial intelligence facilitates the life of people. Ahmad et al [5] investigated the application of artificial intelligence in improving the service quality of computer network and proposed an artificial neural network model to dynamically control the preemption rate of on-going call in the network which starts using Quality of Service (QoS). The model maps network traffic parameters and desired operating pre-emption rate by network operator providing the best for the network under consideration into appropriate tuning parameter.…”