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290 This le is c 1977 and 1983 by McGraw-Hill and c 1986, 2001 by Gio Wiederhold
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(page 291)
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But then chie y do they disdain the unhallowed crowd, as often with their triangles, quadrangles, circles, and the like mathematical devices, more confounded than a labyrinth, and letters disposed one against the other, as it were in battle-array, they cast a mist before the eyes of the ignorant Nor is there wanting of this kind some that pretend to foretell things by the stars, and make promises of miracles beyond all things of soothsaying, and are so fortunate as to meet people that believe them Desiderius Erasmus (1509) on philosophers, from The Praise of Folly
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INTRODUCTION
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Throughout this book we have analyzed disks, le organizations, and transaction patterns So we could proceed rapidly in the prior chapters, details of the methods used for the analyses have been ignored Some of the techniques will now be covered in this chapter, always in the context of le design The analytical techniques applied when designing data-processing systems are derived from applications in areas beyond the subject of this book Many important tools are available from the eld of statistics Statistics may have been applied rst to agriculture, but the techniques can be used in all elds where events are frequent and varied In order to apply statistical techniques, the distribution patterns of the events which are to be analyzed have to be understood A typical collection of events are the transactions placed on the le system The lengths of elds in variable-length records will also show a certain distribution pattern 291
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Section 6-1 discusses types of distributions which are frequently encountered and applies basic statistical tools to some le problems Section 6-2 introduces simulation as a means to obtain performance estimates A high level of demand for service leads to delays and generates waiting lines or queues which are discussed in Sec 6-3 Some aspects of queuing theory and other disciplines from the area of operations research are touched upon in Sec 6-4 The objective of this chapter is to demonstrate the availability of well-developed tools which should be in the satchel of a serious computer system designer To make the techniques easy to use, many algorithms are presented in programmed form The casual reader may skim over the topics in this chapter and proceed to the remainder of this book, which will address general issues of system design and operation
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STATISTICAL METHODS
Analysis and design often can make use of information collected from the observation of the operation of related systems Statistics provides a means to summarize and simplify detailed data The results which are obtained allow transfer of the experience to improved or new systems In order to apply statistical techniques to a process, a number of sequential activities must take place: 1 The process being analyzed has to be understood, so that a model can be built 2 Parameters which a ect the operation will be listed 3 Data about the operation will be a Collected b Displayed with the relevant parameters to identify patterns 4 The cause of patterns seen in the data values will be explained in terms of the parameters used 5 Transfer functions will be developed to permit the application of the collected information to new systems 6 The validity of the transfer will be tested in the new setting We will touch upon these points while using examples from le system applications The actual knowledge of statistics should be obtained from a course or textbooks in this area The section is kept simple so that a background in statistics is not needed to follow the arguments In the preceding analyses of le systems a number of basic parameters were used to explain the operation of les The set of functions or logical rules using these parameters form the model for the systems being described If the system being observed can be described with the parameters used throughout this book, then this description provides an adequate model A model should be complete, that is, it should include all the variables that contribute to the observed results In practice, completeness is di cult to achieve We are happy if a model describes the result modestly well; in engineering sciences, obtaining 20% precision is often satisfactory The remainder is the unexplained portion of the result, comprising all e ects of variables which were not considered in the model
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