b) Passive Monitoring which is described in on next page. Fig. 4.4. Passive monitoring analysis: (a) Performance model for passive monitoring. (b) Typical asymptotic bounds. 4.3.1 Analysis using Passive Monitoring In this approach, no instrumentation code is introduced in the server system. Instead, we use a client load generator to stress test the server and observe two different metrics the — response time obtained by individual clients and the number of clients that can simultaneously served by the system without significantly impacting the performance of the clients. We model the Rover system as a single-server multi-client system as shown in Figure 4.4(a). The Rover System is modeled as a central subsystem consisting of two devices, the Rover controller and the Rover database, and N terminal subsystems. Each of the N terminals is a client of the Rover System and perform the cycle of issuing a request, waiting for the response and processing the response (think time). Wireless network models the communication channel between the server and the clients. Since we are interested in assessing the performance of the Rover system we do not explore the affects of communication channel in this paper. Using a technique called [10] to analyze such systems, it can be shown that the operational law response time observed by clients increase marginally with increasing the number of clients up to a critical client population. Let denotes the time required by the system to process a single D client operation, and denotes the time required at the bottleneck server of a multi-server Dmax system. For the single server model, = If indicates the critical number of clients Dmax D. N* that the system can support without impacting the response time for the clients and the Z think used by the clients between operations, then operational analysis suggests that: time =(D + N* Z)/Dmax The graphical representation of is shown in Figure 4.4(b). N* Experiment Configuration 4.3.2.2
b) Passive Monitoring which is described in on next page. Fig. 4.4. Passive monitoring analysis: (a) Performance model for passive monitoring. (b) Typical asymptotic bounds. 4.3.1 Analysis using Passive Monitoring In this approach, no instrumentation code is introduced in the server system. Instead, we use a client load generator to stress test the server and observe two different metrics — the response time obtained by individual clients and the number of clients that can simultaneously served by the system without significantly impacting the performance of the clients. We model the Rover system as a single-server multi-client system as shown in Figure 4.4(a). The Rover System is modeled as a central subsystem consisting of two devices, the Rover controller and the Rover database, and N terminal subsystems. Each of the N terminals is a client of the Rover System and perform the cycle of issuing a request, waiting for the response and processing the response (think time). Wireless network models the communication channel between the server and the clients. Since we are interested in assessing the performance of the Rover system we do not explore the affects of communication channel in this paper. Using a technique called operational law [10] to analyze such systems, it can be shown that the response time observed by clients increase marginally with increasing the number of clients up to a critical client population. Let D denotes the time required by the system to process a single client operation, and Dmax denotes the time required at the bottleneck server of a multi-server system. For the single server model, Dmax = D. If N* indicates the critical number of clients that the system can support without impacting the response time for the clients and Z the think time used by the clients between operations, then operational analysis suggests that: N* =(D + Z)/Dmax The graphical representation of N* is shown in Figure 4.4(b). 4.3.2.2 Experiment Configuration