 behavior within a small and large time scale. Since self-similarity is believed to have a significant on network and queuing performance, the measurement of self-similarity is important. There are several methods to represent the degree of self-similarity such as variance-time plot, R/S analyze and periodogram-based analysis. Here propose R/S analyze to measure the self-similarity. Given an empirical time series (X : i = 1,2,...,N) of length N , the rescaled adjusted range statistics R(u)/S(u) of values U is given by (A-3) (A-4) Here X(u) is the sample mean. S2(u) is the sample variance. Hurst found that many naturally occurring empirical records are well represented by relation below. (A-5) The positive constant is independent with U. For u 80, the Hurst parameter H 12 0.73. It has been shown that the above relation holds for short-range dependent processes with H = 0.5 and for increment processes of self-similarity with 0.5<H <1. This is generally referred as the Hurst effect. Now we make logarithm of this relation in equation (A-6). A.8 ATM Workstation and Server H . log(u)+log(c) (A-6) The Hurst parameter H can be obtained by the skewness of the line (log(u),log(R/S)) A.8 ATM Workstation and Server The OPNET ATM workstation model represents an ATM node with client-server applications running over TCP and UDP. This model features an application layer that resides over the IP layer, which in turn resides over the ATM layer. The following summarizes the process modules in the ATM workstation/server model. The Application module generates different application traffic, like ftp, http, e-mail, voice, The Transport Adaptation Layer (TPAL) module suite presents a basic, uniform interface between applications and different transport protocols. TPAL also provides a global registry of servers. The TCP and IP modules are working as TCP/IP protocol suite. The IPAL Module transports IP datagram across the ATM network; establishes and releases AAL connections as required. The ATM Layer module encapsulates and forwards dat