FUZZY CONNECTION ADMISSION CONTROL FOR ATM NETWORKS BASED ON POSSIBILITY DISTRIBUTION OF CELL LOSS RATIO

This paper proposes a connection admission control (CAC) method for as ynchronous transfer mode (ATM) networks based on possibility distribut ion of cell loss ratio (CLR), The possibility distribution is estimate d in a fuzzy inference scheme by using observed data of CLR, This meth od makes possible secure CAC, thereby guaranteeing the allowed CLR, In this paper, first, a fuzzy inference method is proposed, based on a w eighted average of fuzzy sets, in order to estimate possibility distri bution of CLR, In contrast to conventional methods, the proposed infer ence method can avoid estimating excessively large values of CLR Secon d, the learning algorithm is considered for tuning fuzzy rules for inf erence, In this, energy functions are derived so as to efficiently ach ieve higher multiplexing gain by applying them to CAC, Because the upp er bound of CLR can easily be obtained from the possibility distributi on by using this algorithm, CAC can be performed guaranteeing the allo wed CLR, The simulation studies show that the proposed method can well extract the upper bound of CLR from the observed data, The proposed m ethod also makes possible self-compensation in real time for the case where the estimated CLR is smaller than the observed CLR It preserves the guarantee of the CLR as much as possible in operation of ATM switc hes, Third, a CAC method which uses the fuzzy inference mentioned abov e is proposed, In the area with no observed CLR data, fuzzy rules are automatically generated from the fuzzy rules already tuned by the lear ning algorithm with the existing observed CLR data, Such areas exist b ecause of the absence of experience in connections, This method can gu arantee the allowed CLR in the CAC and attains higher multiplex gain a s much as possible, The simulation studies show its feasibility, Final ly, this paper concludes with some brief discussions.