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details by simulation as shown below We assume that each scheme here can guarantee no data loss in the network 72241 Basic Assumptions With reference to Figure 713, we consider two different network models in our simulation Each network model is composed of ten hosts and four network nodes Without loss of generality, host i is the source of VC i and consists of its CPN, for 1 F i F 10 Each VC passes node 4, as indicated by the arrows in Figure 713 The shaded nodes node 1 in the first model and node 4 in the second have the common bottleneck link for every VC in the respective models Such models can be used to investigate, in an analogy, how the flooding water in converse situations can be controlled across a series of dams under various flow control strategies.

Also, when you hover your cursor over a tool, Photoshop tells you the name of the tool and how to select it from the keyboard. I explain more about keyboard shortcuts in s D and E on the CD-ROM. If you find the tool tips irritating, turn to General preferences to find out how to turn them off.

Assume that all links have the identical capacity C s 1 cell per unit time For simplicity, let the cycle F s 1 The simulation time is 10,000,000 An on off data source w41x characterized by S, r, L is used, where S is the peak rate, r the average rate, and L the mean burst length of the VC We also assume that S s C for each VC The throughput per VC is measured at node 4 as the total number of cells it has dispatched during the simulation time On the other hand, each host and node has an infinite buffer, so there is no data loss from end to end Moreover, there is also an infinite buffer at the downstream neighbor of node 4, so that node 4 can send data as fast as possible under various flow control strategies.

8.6.1. An Example of Reactive Planning In the following, we illustrate how a mathematical model can be applied usefully also in a reactive planning process in order to take into account some new information. This illustration is based again on the simple job scheduling problem described in Section 8.2. Again, we consider the instance of this job scheduling problem shown in Table 8.1. We also assume that in the proactive planning process the initial plan shown in Figure 8.2 was generated. This initial plan is now considered as part of the input of the reactive planning process, and it is represented by the parameters aj,m for j 1, . . . , J and m 1, . . . , M. That is, aj,m 1 if and only if in the original plan job j is to be processed by machine m. Next suppose that, after this initial plan has been generated, one realizes that the processing time of job 6 has been estimated too optimistically. A better estimate seems to be 4 hours instead of 3 hours. Furthermore, also a rush order with job number 7 and a processing time of 3 hours is to be taken into account. One way to incorporate this additional information into the model is represented in Figure 8.10. This model is basically the same as the model described in Section 8.2. The only difference is found in the objective function. Due to this modi ed objective function, the model focuses on staying as close as possible to the original plan. Indeed, if aj,m 1, then a value 1 for the decision variable Aj,m is preferred

Identical nodal buffer allocation is used for each scheme The feedback information will not experience waiting delay, but only the link propagation delay For comparison, let us choose the minimum bandwidth provided by the GPS service policy to be the agreed constant rate specified in both the BBFC and the EASY For the FCVC, assume that each node will send a feedback cell to its upstream node once it dispatches a data cell for each VC For the.