TY - JOUR
T1 - Neural-network-based call admission control in ATM networks with heterogeneous arrivals
AU - Hah, Jen M.
AU - Tien, Po-Lung
AU - Yuang, Maria C.
PY - 1997/9/8
Y1 - 1997/9/8
N2 - Call Admission Control (CAC) has been accepted as a potential solution for supporting diverse, heterogeneous traffic sources demanding different Quality of Services (QOSs) in ATM networks. Also, CAC is required to consume a minimum of time and space to make call acceptance decisions. In this paper, we present an efficient neutral-network-based CAC (NNCAC) mechanism for ATM networks with heterogeneous arrivals. All heterogeneous traffic calls are initially categorized into various classes. Based on the number of calls in each class, NNCAC efficiently and accurately estimates the cell delay and cell loss ratio of each class in real time by means of a pre-trained neutral network. According to our decent study which exhibits the superiority of the employment of analysis-based training data over simulation-based data, we particularly construct the training data from a heterogeneous-arrival dual-class queueing model M[N1] + I[N2]/D/1/K, where M and I represent the Bernoulli and interrupted Bernoulli processes, and N1 and N2 represent the corresponding numbers of calls, respectively. Analytic results of the queueing model are confirmed by simulation results. Finally, we demonstrate the profound agreement of our neural-network-based estimated results with analytic results, justifying the viability of our NNCAC mechanism.
AB - Call Admission Control (CAC) has been accepted as a potential solution for supporting diverse, heterogeneous traffic sources demanding different Quality of Services (QOSs) in ATM networks. Also, CAC is required to consume a minimum of time and space to make call acceptance decisions. In this paper, we present an efficient neutral-network-based CAC (NNCAC) mechanism for ATM networks with heterogeneous arrivals. All heterogeneous traffic calls are initially categorized into various classes. Based on the number of calls in each class, NNCAC efficiently and accurately estimates the cell delay and cell loss ratio of each class in real time by means of a pre-trained neutral network. According to our decent study which exhibits the superiority of the employment of analysis-based training data over simulation-based data, we particularly construct the training data from a heterogeneous-arrival dual-class queueing model M[N1] + I[N2]/D/1/K, where M and I represent the Bernoulli and interrupted Bernoulli processes, and N1 and N2 represent the corresponding numbers of calls, respectively. Analytic results of the queueing model are confirmed by simulation results. Finally, we demonstrate the profound agreement of our neural-network-based estimated results with analytic results, justifying the viability of our NNCAC mechanism.
KW - Call Admission Control (CAC)
KW - Cell delay
KW - Cell loss ratio
KW - Heterogeneous-arrival queueing model
KW - Neutral network
KW - Quality of Service (QOS)
UR - http://www.scopus.com/inward/record.url?scp=0031559622&partnerID=8YFLogxK
U2 - 10.1016/S0140-3664(97)00101-1
DO - 10.1016/S0140-3664(97)00101-1
M3 - Article
AN - SCOPUS:0031559622
SN - 0140-3664
VL - 20
SP - 732
EP - 740
JO - Computer Communications
JF - Computer Communications
IS - 9
ER -