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For instance, if indexed les are to be used, we can determine which attributes should be indexed We can also optimize space-time trade-o s for direct les Given a number of assumptions, we can show that in a database a global optimum can by achieved while optimizing the individual les separately, one by one In distributed systems the optimal assignment of les or parts of les can also be determined References to a number of such models can be found at the end of this chapter Unfortunately, all these methods have to make many assumptions about the available alternatives, so that their model tends to be simpler than the real world Many of them are also mathematically complex Their use will provide an excellent starting point for further determinations and will provide insight into complex design choices The second approach to select les follows conventional engineering practice We assume here either that the choices are practically limited or that a prior modeling analysis has narrowed down the set of alternatives The process begins with the selection of candidate hardware systems which can deal with the storage requirements estimated earlier These are then compared and scaled up or down according to the degree of over- or underutilization which we perceive during the analysis It is not unusual in database work that the smallest computer adequate to store the data quantity and support the le programs is adequate to perform the remaining tasks On the other hand, it does happen that there is no single computer capable of dealing with the data ow between core storage and disk which might be required in an active database environment Then the tasks will have to be distributed over multiple processors After an approximate system is selected, a le design is chosen which matches the expected utilization pattern and can be supported using vendor and in-house resources The procedure to establish the adequacy of the selected computer system is iterative: 1 The demand on the system is estimated 2 The available resources of the system are similarly estimated 3 The two are compared to see if a satisfactory match is obtained A match is satisfactory when the demand is less than the capability of the resources by a ratio which is a function of the precision used to make the estimates 4 If the demand is not comfortably within the capability of the resources, then both the demand and the resources are reestimated more precisely, and a new comparison (3) is made 5 If there is a major mismatch, the equipment selected, the design of the les, or the tasks to be performed are changed and the design process is restarted at 2 or 1 In order to simplify the exposition, we will begin with the assumption that the application using the le system will be the only use of the computer Consideration of the e ect of multiprogramming will appear in Sec 5-4-3 The measurement used to appraise the demand and the capability of the system is time We will not worry in the presentation about the unit to be employed; in practice, it seems reasonable to scale all values for on-line systems to minutes.

Sec 5-4

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5-4-1 Aggregate System Demand The performance parameters, which were derived from the le design, provide estimates of the time required to complete the various transactions These are multiplied by the estimates of the transaction loads to obtain the gross aggregate demand Table 5-1 provided the rules for matching load parameters to systems The performance parameters used here assume that this has been done We will perform the initial demand calculation for a period of high demand If the daytime activities consist of searches and updates, and insertion of new data and exhaustive searches are delegated to evening operation, then the desired production during the daytime hours is computable as the sum of the activity times for all transactions to be carried out qtotal = LF TF + LN TN + LU TU + LZ TZ 5-2

1

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