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Here we consider several memoryless on=off sources, each of them served by a server. Each service time is exponential with a mean of 1 time unit, and the peak rate of each source is l > 1. Therefore, the probability generating function g z of the queue length distribution is simply g z



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This section outlines some general principles for optimizing code. These do not attempt to say anything new; however, restating the obvious is not always a bad thing. get the design right The biggest gains generally come from getting the overall architecture and design right: how operations should be split between server and client, what technologies to use (e.g. messaging, RMI, object database or relational database), what hardware to use, even what languages are used. It is important to design to interfaces, not implementations. This makes it easier to slot in a different or improved algorithm: for example, depending on your data size and how it might already be sorted, there are times when a pigeon sorting algorithm will be the best choice, and times when a bubble sort will be appropriate. optimize late: optimizing too early in the process means that you will produce intricate code that gets in the way of good design optimize only where necessary: nd out where the bottlenecks are and concentrate on sorting them out; this requires access to suitable pro ling and heap analysis tools do not over-optimize. The more you optimize your code, the more highly tuned it becomes to the particular environment. If the environment changes or you want to use the code in a different application, it may run more slowly. Compiler technology in particular can have a profound effect on the bene ts or otherwise of a particular optimization. Optimization can often con ict with other goals for the code: clarity and maintainability: improving performance at the code level generally (though not always) means writing more, and often quite obscure, code (we shall see an example of this in the case study in Section 7.14)





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14 Jun 2018 ... Simple Tesseract OCR — Java . Step#1: Download tessdata [eng.traineddata] Step #2: Get a sample image (Grayscale converted) with something written on it. Step#3: Add the below dependency in the pom.xml- Step#4: Write the below code snippet to perform OCR - Step#5: On executing the above code, the output is displayed on ...

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where qi z is the probability generating function of the queue assigned to source number i. To simplify our analysis we look at a set of self-similar on=off sources. The vector n1 ; n0 of each source is collinear to a xed vector v1 ; v0 : for source i, n1 ; n0 ei v1 ; ei v0 . Referring to Theorem 11.3.1, we see that the probability that queue i contains more than n packets is exactly lv1 1 n z ei v1 v0 1 with z1 x given by Eq. (11.12) with substitution of n1 ; n0 by xv1 ; xv0 . If we denote by r e the asymptotic density of sources with vector n1 ; n0 equal to ev1 ; ev0 , then we have the following result [14]. Theorem 11.3.3. Let 0 < b < 1. If function r x mxb 1 O xb 1 e when x 3 0, then when N 3 I quantity pn converges to the following expression: b lv1 v1 v0 mn b O n b e : v1 v0 l 1 11:14

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of the edge on the rst link and an earlier start on the last link, so that the involvement is scheduled earlier (on the source processor) or later (on the destination processor) than the network activity An extract of a corresponding schedule is depicted in Figure 89 Due to contention, the start of edge eAB is delayed on the links and on P2 so that the alignment with the involvement on the processors is not given This relaxation corresponds to the nonaligned scheduling approach in edge scheduling (Section 731) An enforced aligned scheduling, where the involvement part of the edge on the processors is aligned with the edge on the links, would result in the same issues as for the aligned edge scheduling approach (Section 731) Many idle time periods would be created on the processors, and the scheduling of an edge, on the processors and links, became more complex The discussion of scheduling algorithms for the involvement contention model in the next section makes this clearer

Proof. Note that the conditions of the theorem are basically equivalent to the fact that the Mellin transform of function r x has a single pole at s 1 b, with residue m, and can be analytically continued on the strip fs; R s P 1 b e; 1 b g. For any xed N we have pn At the limit we have lv1 lim pn M 3I v1 v0 I

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z1 y 1 n r y dy:

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