ORIGINAL ARTICLE Application of artificial intelligence to improve quality of service in computer networks Iftekhar Ahmad • Joarder Kamruzzaman • Daryoush Habibi Received: 7 July 2009 / Accepted: 3 May 2011 / Published online: 22 May 2011 Ó Springer-Verlag London Limited 2011 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 pre- emption 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 appro- priate 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. Keywords Neural networks  Computer networks  Quality of service  Call preemption 1 Introduction For the past decades, quality of service (QoS) provisioning has been a major research topic mainly because of increasing demand of multimedia and distributed applica- tions. Resource reservation is one of the widely practiced techniques that are used to ensure guaranteed QoS of applications. Two types of reservation techniques have been proposed by researchers: (i) book-ahead (BA) reservation (ii) instantaneous request (IR) reservation. Multimedia and distributed applications that require long duration, high bandwidth demand, and have time-sensitive significance are good candidates for book-ahead reservation [1–5]. In BA reservation, resource is reserved well in advance from the announced starting time over the declared duration to ensure that the application will not experience scarcity of resource at the point of its activation. Contrarily, an IR call connection requests for immediate reservation and usage of resources. Resource sharing between BA and IR requests imposes a number of challenges. One of them is to keep the preemption rate of ongoing IR calls very low as preemption is considered as an interruption to service continuity and is perceived as a serious issue from users’ perceived QoS point of view [6, 7]. In this paper, a novel application of ANN is shown to maintain service continuity of ongoing IR calls in a QoS-enabled network with provision for BA reservation. Artificial neural network (ANN) has been a useful tool to solve a number of complex problems in relation to quality of service (QoS) provisioning in communication research. Neural networks have been successfully used in solving problems like routing, resource allocation, call admission control, traffic pattern estimation, packet loss estimation, and network parameter control. Chou and Wu [8] proposed a neural network–based model that adaptively I. Ahmad (&)  D. Habibi School of Engineering, Edith Cowan University, Joondalup, WA, Australia e-mail: i.ahmad@ecu.edu.au J. Kamruzzaman School of Computing and Information Technology, Monash University, Melbourne, VIC, Australia 123 Neural Comput & Applic (2012) 21:81–90 DOI 10.1007/s00521-011-0622-6