Networks protocols are implemented in combinations of software, firmware, and hardware on each end of a connection. Introduction of multiservice networks demands more intelligent control over network usage and more efficient application development practices that enable achieving Quality–of-Service (QoS) goals. As applications over the network increase, so does the need to diagnose performance at the application level, knowing how traffic patterns are affected in terms of guaranteed bandwidth, delay and reliability. In this chapter, the concept for testing multiservice networks in real-life situations during stationary time intervals, by using expert-system-based protocol analysis, is described. The architecture of hardware and software needed for data acquisition and testing, that, as a working example, was conducted on a major network with live traffic, is proposed, as well as the appropriate algorithm for estimating standard QoS parameters from the measured data that mainly included decoding with precise time-stamps and expert-system comments, resulting from the appropriate processing of the network data.

In this paper, a solution is proposed for testing TCP congestion window process in a real-life network situation during stationary time intervals. With this respect, the architecture of hardware and expert-system-based distributed protocol analysis is presented that we used for data acquisition and testing, conducted on a major network with live traffic (Electronic Financial Transactions data transfer), as well as the appropriate algorithm for estimating the actual congestion window size from the measured data that mainly included decoding with precise time-stamps (100ns resolution locally and 1ms with GPS clock distribution) and expert-system comments, resulting from the appropriate processing of the network data, accordingly filtered prior to arriving to the special-hardware-based capture buffer. In addition, the paper presents the statistical analysis model that we developed for the evaluation whether the data belonged to the specific (in this case, normal) cumulative distribution function, or whether two data sets exhibit the same statistical distribution - the conditio sine qua non for a TCP-stable interval. Having identified such stationary intervals, it was found that the measured-data-based congestion window values exhibited very good fitting (with satisfactory statistical significance) to the truncated normal distribution. Finally, an appropriate model was developed and applied, for estimating the relevant parameters of the congestion window distribution: its mean value and the variance.

This paper studies how end-to-end application peiformance(of Electronic Financial Transaction traffic, in particular)depends on the actual protocol stacks, operating systemsand network transmission rates. With this respect, the respectivesimulation tests of peiformance of TCP and UDP protocolsrunning on various operating systems, ranging from Windows,Sun Solmis, to Linux have been implemented, and thedifferences in peiformance addressed focusing on throughputand response time.