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The traf c processes determined by Eqs (136) and (137) are LRD processes [1, 6, 11], henceforth referred to as LRD process I and LRD process II, respectively We use Eq (137) as an example to show that the assumption of the Pareto distribution in Theorems 1336 and 1337 is not restrictive as far as the impact of long-range dependence is concerned What is essential here is that the distribution is heavy tailed, regardless of the speci c form of the distribution Clearly, the process determined by exponentially distributed S and T is a Markov process Therefore, the queueing system with such a Markov arrival process can also be modeled by a Markov process However, the queueing system with an LRD arrival process is not Markovian anymore To be speci c, we let a 1:6, g 0:5, r1 1, and r0 0.



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If you choose to keep a spare parts inventory on hand, there are certain policies to follow that will help make your decision especially fruitful: Inventory the parts that fail the most This will have the greatest impact on MTTR, as you will not have to wait for shipping You can replace these items as soon as they fail (or even before they fail, if you keep a close eye on system logs; see Preventative Maintenance) Disks and fans have moving parts, and as such fail a lot A large system will have hundreds or even thousands of disks; if you are responsible for their maintenance, it is wise to keep spare disks around so that the failure of a single disk doesn t put a large chunk of critical data at risk Part for part, though, power supplies fail the most often, so you should definitely stock them.





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Since all traf c processes considered have the same E S and E T , and S and T are identically distributed, the expectations determined by Eqs (136) and (137) are the same So the parameter A in Eq (137) can be determined by the above parameters As shown in Section 13323, by exploiting the renewal property of two-state uids, we can investigate the transient loss behavior of the queueing system by examining the values of performance functions P w and E w as w varies in 0; B We consider three traf c processes determined by the distributions of S and T as described above The assumption that all traf c processes considered have the same E S and E T implies that the marginal state distributions of the traf c processes are the same.

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Tesseract is an optical character recognition engine for various operating systems. It is free software, released under the Apache License, Version 2.0, and development has been sponsored by Google since 2006. In 2006, Tesseract was considered one of the most accurate open-source OCR ... History · Features · Reception

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It doesn t hurt to have some spare monitors on hand, either; there s no value in cannibalizing a good desktop system because you need a head for a critical system Inventory parts that are hard to get If you find that a particular part is always back-ordered when you need it, it makes an excellent choice for inventorying If you can wait for the part when there is no immediate need for it, everyone will be happier Test your stock When parts come in, make sure that they work in noncritical systems If they don t, there is no point to keeping them in inventory Return defective parts before they are needed rather than when systems are down and waiting for them Rotate your stock Always put the oldest parts into systems, rather than the newest parts.

We rst let buffer size B 1 and service rate C 0:8 for all the traf c processes, and compare transient loss behaviors of the traf c processes based on the functions P w and E w computed, respectively, according to Eqs (138) and (139) in Section 1371 We observe that even though the marginal state distributions of the traf c processes are the same, the transient loss measures of the traf c processes can be.

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Fig. 13.2 Transient loss behaviors of different traf c processes. (a) The loss probability with B 1 and C 0:8. (b) The expected loss ratio with B 1 and C 0:8. (c) The loss probability with B 1 and C 0:95. (d) The expected loss ratio with B 1 and C 0:95.

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