K. Watkins, Discrete Event Simulation in C, McGraw-Hill, 1992.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....illustrates several important features of FPGA queue simulation. Figure 4 shows the performance of the simulator relative to an equivalent software simulator run on an IBM compatible 486DX2 66 MHz PC. The software simulator was programmed using the C based CSIM discrete event simulation libraries [9]. It can be seen that the FPGA simulator has significantly faster simulation run times. Due to the increased number of customers in the queue as the load approaches unity, the software simulator has a much larger event list to process and consequently, simulation slows down. Since no such ....

K. Watkins, Discrete Event Simulation in C, McGraw-Hill, UK, 1993

....represented via the stochastic parameters of the model. Machine breakdowns are truly stochastic events that happen at non uniform time intervals, and historical data are necessary to predict this behaviour. A stochastic parameter is used to model the time of a breakdown via the Monte Carlo method [6, 29]. Two other stochastic parameters concern scrap quantities and the time an operator works. Both parameters are represented by proper probability distributions, such as the Gaussian, the Weibull, the Gamma and the Pearson distribution [6] given their average value and its variance. Breakdown data ....

Watkins K., "Discrete Event Simulation in C", McGraw-Hill, 1993.

....based on an uniform probability, and (iii) an open model simulating a n x n multistage omega interconnection network (MIN) 3. 1 Elapsed Time For purpose of comparison, we wrote the simulation benchmarks using the Simscript [11] simulation language and in the C based simulation library by Watkins [17]. Table 3 compares these implementations C library (denoted by CSim) Simscript and SPaDES Co. The program code and executable file size for SPaDES C is the smallest among all the three implementations. Table 4 depicts the simulation ran time on the Fujitsu AP3000. The sequential simulators ....

K. Watkins, "Discrete Event Simulation in C," McGraw-Hill Book Company, 1993. 93

....have con structed the simulators for two examples: i) timeshared computer system [8] figure 6) and (ii) cafeteria chain (figure 7) In figure 7 each number shown in a server station indicates the number of servers po sitioned in the station. The sequential simulators are programmed in CSim [19] and Simscript [14] while their parallel versions in SPaDES C . Due to space constraint, we present the performance obtained from the Fujitsu AP3000 parallel computer only. Let p denotes the number of nodes used in parallel simulation. As shown in table 1, the source program code sizes required ....

K. Watkins, "Discrete Event Simulation in C," McGraw-Hill Book Company, 1993.

....for its efficiency. 6.2 Traffic Overhead As could be seen from figure 4, MMB may produce more messages than normal RPF. In order to quantify this overhead we simulated random networks once more using the Waxman model and performed discrete event simulation using the package provided by Watkins [20]. Nodes are selected randomly to join the network and events corresponding to MMB messages are triggered. We used 200 random networks with 10 runs for each. The results can be seen in figure 9 where we plotted the average number of messages per link in each join. The first members produce more ....

Kevin Watkins. Discrete Event Simulation in C. McGraw Hill International, 1993.

....of keys migrated and their key range values when a branch is detached from the B tree in the source PE and attached to the B tree in the destination PE. This information is captured at each migration and used in the second phase. 2. Phase 2. Here, we use the simulation package, CSIM [17], which easily allows us to measure the response time of the queries and the number of queries waiting in the queue. We model each of the PEs as a resource and the queries as entities. We use the same 10000 queries generated using the zipf distribution. The migration of a branch in a hot PE to ....

K. Watkins. Discrete event simulation in c. McGrawHill, 1993.

....In addition, delays between state changes are usually represented by statistical distributions. 2.1. 1 Components of Discrete Event Simulation Researchers have identified several components common to all discrete event simulation in the past several decades (Kreutzer, 1986; Law, Kelton, 1991; Watkins, 1993): 1. Model structuring and execution facilities. As stated previously, a model is a collection of entities interacting with each other. Object oriented languages, such as C (Stroustrup, 1991) and Java (Flanagan, 1996; Naughton, 1996) allow us to represent entities as objects, relationships via ....

Watkins, K. (1993). Discrete event simulation in C. New York, NY: McGraw-Hill.

....may need additional means to engage in an operation or task. BUSINESS PROCESS SIMULATION The BPSIM library is an extension of the simulation library SIM (Bolier and Eliens 1994) SIM is a C library offering classes supporting discrete event simulation, based on standard simulation techniques (Watkins 1993). In discrete event simulation, the components of the model consist of events, which are activated at certain points in time and in this way affect the overall state of the system. The simulation library consists of the following classes: simulation the scheduler event representing ....

K. Watkins. Discrete event simulation in C. McGraw-Hill, 1993.

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K. Watkins, Discrete Event Simulation in C, McGraw-Hill, 1992.

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Watkins K., "Discrete Event Simulation in C", McGraw-Hill, 1993.

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