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Clearly, it depends upon the CLP In Example 41, the statistical multiplexing gain is a factor of almost 5 The special case of requiring CLP D 0 is usually referred to as deterministic multiplexing Example 41 (Statistical multiplexing) Consider a discrete-time model of an unbuffered link that can carry 950 cells per epoch There are x identical sources In each epoch each source produces between 0 and ve cells; suppose the number is independently distributed as a binomial random variable B5; 0:2/ Thus, h D 5 and xpeak D 950=5 D 190 The mean number of cells that one source produces is m D 5 0:2 D 1, and the number of cells that 900 sources produce is approximately normal with mean 900 and variance 0:2 0:8 900.

FIGURE 7.19 Not all dimensions and metrics can be used together. Here the Visitor dimension has locked down use of almost all the other metrics and dimensions because the Visitor dimension is compatible only with dimensions related to time. Note that grayedout dimensions in the list can t be dragged into the Custom Report building canvas because they aren t compatible with the selected metric of Visitors.

Universal Mobile Telecommunications System (UMTS)

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From this we calculate that the probability that 900 sources should produce more than 950 cells in a slot is about 00000155 Thus for a CLP of 1:55 10 5 , we can take xstat D 900 and there is a statistical multiplexing gain of 900=190 D 4:74 This gain increases as the capacity of the link increases For example, if C is multiplied tenfold, to 9500, then 9317 sources can be multiplexed with the same CLP of 00000155 The SMG is now 9317=1900 D 4:90 As C tends to in nity the SMG tends to h=m D 5 As we will see in Section 412, some multiplexing gain is possible even if we require CLP D 0.

In Windows, the developer needs to carefully target the minimum operating system con guration on which their application will run and then select the APIs available for use on that platform. For example, the NT

(1.64)

If Zm is real, so Zm = Rm , Eq. (16) can be combined with Eq. (13) to give SWR = which is SWR = MAX Rm + R0 + |Rm R0 | , Rm + R0 |Rm R0 | Rm R0 , . R0 Rm (18)

For example, if sources are policed by leaky buckets and links are buffered, then it is possible to carry more connections than would be allowed under the peak rate constraint of (42) 43 Accepting calls Consider a network comprising only a single link Suppose that contracts specify exact traf c types and that there are xi contracts of type i, with i D 1; : : : ; k Suppose that the only contract obligation is the QoS constraint CLP p, for say p D 10 8 The technology set A, which we also call the acceptance region, is that set of x D x1 ; : : : ; x k / corresponding to quantities of traf c types that it is possible to carry simultaneously without violating this QoS constraint (see Figure 42).

This book provides a logical and straightforward approach to developing CWM solutions. The following is a roadmap to the chapters of this book. Readers who are generally familiar with CWM concepts might consider skipping the first two chapters and begin with 3, Modeling Meta Data Using CWM. Those who are already comfortable with the approach taken by CWM in modeling meta data solutions might also consider skipping 3 and begin with 4, Meta Data Interchange Patterns. 3 introduces substantial new material that forms the basis for much of what follows in the rest of the book. Part One, Introduction, provides all necessary introductory material required for the book as a whole:

1.5 PLANAR APERTURE SYNTHESIS The previous section was devoted to the syntheses of linear arrays and continuous line sources. In this section we will consider syntheses of planar sources, in particular circular sources. For a circularly symmetric source distribution, the pattern function can be expressed in terms of two-dimensional Fourier transform as below:

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