As the awareness of patterns has grown in the software community, an increasing amount of work has focused on pattern languages [Cope96] [Gab96] [Cope97]. As early as 1995 leading authors in the pattern community began documenting collections of patterns for specific software development domains [PLoPD1], particularly telecommunications [DeBr95] [Mes96] [ACGH+96] [HS99b]. These patterns were more closely related than the standalone patterns published earlier. In fact, some patterns were so closely related that they did not exist in isolation. The patterns were organized into pattern languages in which each pattern built upon and wove together other patterns in the language. Many of the published pattern languages appear in [Beck97] [PLoPD2] [PLoPD3] [PLoPD4]. As we demonstrate in Section 6.2, A Pattern Language for Middleware and Applications, the patterns in this book also form a pattern language, or more precisely the core of a pattern language, for developing distributed object computing middleware, applications, and services. Our prediction that work on pattern languages would expand has therefore also come true. Again, however, significant gaps remain in the literature, due in large part to the effort required to write comprehensive pattern languages. There has been work on classifying patterns and organizing them into categories [Tichy98], pattern systems [POSA1], and the comprehensive Pattern Almanac appearing in [Ris00a]. In general, however, pattern languages have become more popular than pattern systems and pattern catalogs during the past four years. The patterns community has found pattern languages to be the most promising way to document sets of closely-related patterns. Section 6.4, Pattern Languages versus Pattern Systems provides further justification and motivation for the gradual transition from pattern catalogs and systems to pattern languages.
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where we are using capital R for the random variable of returns as opposed to lowercase r for the realizations. Such a distinction is not always convenient to sustain. Hence the quantiles for R are obtained by a linear transformation of the quantiles of z, the standard normal. Ra = m + Za s, (2.1.2)
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Medications: stimulants such as methylphenidate (Concerta, Ritalin) Medications: clonidine (Catapres) Medications: tricyclic antidepressants Medications: SSRIs
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where xi is the number of connections of type i that use the link, h i is the maximum rate of cells that the service contract allows to service type i, and C is the capacity of the link. Although such a constraint makes sense for synchronous networks, in which connections are allocated xed amounts of bandwidth during their lifetimes, equal to their peak rates h i , it may not make sense for asychronous networks, where connections are allocated bandwidth only when there is data to carry. If the service provider of such a network uses (4.2) to de ne the technology set he does not make ef cient use of resources. He can do better by making use of statistical multiplexing, the idea of which is as follows. Typically, the rate of a traf c stream that uses service type i uctuates between 0 and h i , with some mean, of say m i . At any given moment, the rates of some traf c streams will be near their peaks, others near their mean and others near 0 or small. If there are many traf c streams, then the law of averages states that the aggregate rate is very likely to be much less than P P i x i h i ; indeed, it should be close to i x i m i . If one is permitted an occasional lost cell, say CLP D 0:000001, then it should be possible to carry quantities of services substantially in excess of those de ned by (4.2). Instead, we might hope for something like
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Fig. 8.2 AM: (a) time, (b) Fourier, and (c) phasor representations. (From Egan, 2000.)
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Add a parametric model or a tool for project estimation; use iterative tuning for successive estimations.
5.3.3 Architecture Changes
Other Range Limitations
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