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Figure 6.2 A monopolist can increase his pro t by price discrimination. Suppose customer A values the service at $3, but customers B, C and D value it only at $1. There is zero production cost. If he sets the price p D $3, then only one unit of the good is (just) sold to customer A for $3. If he sets a uniform price of p D $1, then four units are sold, one to each customer, generating $4. If the seller charges different prices to different customers, then he should charge $3 to customer A, and $1 to customers B, C and D, giving him a total pro t of $6. This exceeds $4, which is the maximum pro t he could obtain with uniform pricing.

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{Let's use the date and extract the information that we need to determine the month and day} {If we devide the date by 10000, the remainder is the month and day. We can use the modulus function which determines the remainder of division} monthAndDay = Mod(Date of tomorrow,10000); if(monthAndDay >= goLongStart and monthAndDay <= goLongEnd) then begin buy("Seasonal Buy") tomorrow at Open; end; if(monthAndDay >= goShortStart or monthAndDay <= goShortEnd) then {Notice that we had to use "or" instead of "and" this is due to the goShortEnd date is less than the goShortStart date} begin; sell("Seasonal Sell") tomorrow at Open; end;

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9.4 SUMMARY AND CONCLUSION Task scheduling is a big issue in a parallel and distributed computing system. The simulation and visualization model presented here only covers some basic elements of the problem. Among other things, this chapter presents the development of a simulation model for scheduling tasks in a dependency graph using a model called the path maximum magnitude (PMM). The simulation model provides a visual interface for drawing a dependency graph, with the magnitude of the tasks and the links determined randomly by the computer. The scheduling method using PMM produces results in the form of Gantt charts, which clearly show the start and completion time of each task in its assigned processor. The scheduling technique includes the precedence relationship between the tasks and their communication costs in the mapping. The PMM method can easily be used as a prototype model for other scheduling methods. The model can be extended to include other parameters in task scheduling, such as factors affecting machine performance. These include the initial startup cost, the transmission rate, the contention rate, and the cabling mechanism. In a real parallel and distributed system, these parameters severely affect the communication cost of transferring information from one point in the network to another. Due to different requirements, some applications may not agree with the PMM method discussed here. In this case, other scheduling techniques may be used instead of PMM. Other than parallel and distributed systems, the techniques discussed here can also be applied to problems involving machine scheduling, transportation, mobile computing, and grid computing. Our simulation model can be applied to cases in which there are similarities in the way the tasks are mapped in these problems.

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Looking at the sum a little closer, we nd that it is made up of coherent pairs since, for each product ei ej that occurs in multiplying one set of voltages by the other, there is a second identical product ej ei . One pair arises from ei in the rst distribution and ej in the second, and the other arises from ej in the rst distribution and ei in the second.2 The voltages add for the two members of each coherent pair whereas the powers add when the various pairs, which are not coherent with each other (i.e., their phase relationships are random), are added. Therefore, the result has twice the power density that it would if everything were noncoherent. [Adding the two members of a pair gives a power four times the individual powers, rather than two times, as noncoherent addition would give. Thus the summation of voltage that occurred for coherent signals (cosines) implies twice the summation of powers.] Therefore, we integrate the product of input PSDs to get a resulting PSD and double the result in recognition of the coherent pairs: S2 (f ) S0 (f ) S0 (f ) =2 , (5.5) 2 2 2 where correlation is indicated by the pentagram and the prime differentiates this function from the same function after it has been multiplied by appropriate constants. This correlation is the same as convolution, since S(f ) = S( f ). As f approaches zero, the correlation becomes equal to the integral of a constant [S0 (0)/2]2 over a width of 2B, so Eq. (5.5) gives S2 (0) = 4B 2 S0 2

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