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The remainder of this chapter is devoted to showing how to do transient analysis with the source traf c odel. We suggest focusing on the future time-dependent mean conditional on the present state. The present state of each level process consists of the level and age (elapsed holding time in that level). Because of the anticipated large number of sources, the actual bandwidth process should be closely approximated by its mean, by the law of large numbers (LLN). As in Duf eld and Whitt [14], the conditional mean can be thought of as a deterministic uid approximation; for example, see Chen and Mandelbaum [8]. Since the withinlevel variation process has mean zero, the within-level variation process has no effect on this conditional mean. Hence, the conditional mean of the aggregate bandwidth process is just the sum of the conditional means of the component level processes. Unlike the more elementary M =G=I model considered in Duf eld and Whitt [14], however, the conditional mean here is not available in closed form. In order to rapidly compute the time-dependent conditional mean aggregate demand, we exploit numerical inversion of Laplace transforms. It follows quite directly from the classical theory of semi-Markov process that explicit expressions can be given for the Laplace transform of the conditional mean. More recently, it has been shown that numerical inversion can be an effective algorithm; see Abate et al. [1]. For related discussions of transient analysis, design and control, see s 13, 16, and 18 in this volume. 17.3 OUTLINE OF THE CHAPTER. Code11 Encoder In .NET Using Barcode encoder for .NET Control . Draw GS1-128 In Visual C#.NET Using Barcode .Related: Printing Codabar .NET , ITF-14 Printing .NET , Make Interleaved 2 of 5 .NET

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