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TCP sockets set a timer that detects failure of either endpoint. The default value for this keep-alive timer can be as long as 2 hours, depending on your operating system; however, many network managers tune it down to several minutes to allow rapid cleanup of broken connections. As a result, a network that takes 2 minutes to reconfigure may result in broken socket connections. Applications don t always wait patiently for initial connections to a service. If an application was attempting to contact a network service on net3 when r4 failed, it s possible that the application will timeout and complain. A subsequent attempt should result in a success, but the user sees a temporary failure. Hosts on net3 may not know about a return path to net1 via router r3. If they were using router r4 as a default route and were not receiving RIP updates, then incoming traffic will reach net3, but responses will be undeliverable because no path exists back to the source on net1.



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Oct 14,2019 • Filed to: OCR • Proven solutions. 0. People may ask what is the best OCR software for Windows 10, after reading this article you will get the ...

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as y 4 I. Up to multiplicative constant l, the latter is equal to the covariance function of the FBM introduced in Eq. (5.48). We therefore conclude that the sequence of Gaussian centered processes fZt y g converges weakly toward the latter Gaussian process. j

When you lose end-to-end connectivity, in either direction, applications on either side believe the other side has failed. Errors that occur during connection setup are easily traced back, but intermediate failures, once connections are established, generate calls to the help desk.





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As noted by Boxma and Dumas [4] in the case of a nite number N of on=off sources, no matter how heavy the tail of the off duration distribution, the distribution N of V0 is light tailed (i.e., has nite moments) whenever the on duration distribution is light tailed. On the other hand, for an on duration distribution with Pareto tail, the N distribution of V0 also has a Pareto tail whatever the off duration distribution. In N contrast, the Weibullian distribution derived in this chapter for V0 as N 4 I in the heavy traf c limit can arise as long as at least one of the two distributions for the on and off periods is heavy tailed. Such an observation is in accordance with the usual fact that ``small'' sources on a link with large capacity perform better than ``large'' sources in terms of multiplexing ef ciency. The amount of multiplexing gain has been derived here by means of powerful tools from weak convergence theory for

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skip over the technical details in favor of sensationalism and attempts to scare their viewers As far as we know, no TV networks employ specialized availability consultants (Your authors can recommend a couple, though, if any networks are interested) Stock price A common follow-on to the bad publicity and press from downtime is that Wall Street may perceive a corporate weakness and sell the company s stock This can have a wide-reaching effect throughout the company, personally affecting everyone all the way up to the CEO Legal liability Stockholders routinely bring lawsuits against companies whose stock has taken precipitous drops Many of these are dismissed as frivolous, but others are not The likelihood of corporate officers being demonstrably liable will increase when there is the perception that those officers of the company did not take appropriate measures to maintain computer system reliability.

random processes and some essential bounds for the distribution of the content of a uid queue An important direction for future work is to consider the details of the convergence with N , so as to know when the results presented here will be relevant in practice For example, for a given moderate to large but nite N , it is desirable to determine at which queue levels, up to some appropriate measure of precision, the Weibullian tail result holds approximately Attempting to answer such questions through simulation would seem to be pointless, due to the small probabilities at issue, combined with the particular dif culties stemming from such LRD processes, such as the simulation of in nite variances Analytical expressions are therefore required This is an even more important question than one might rst realize, as the following discussion makes clear.

Let the output rate C be held constant and the source rates scaled down instead with increasing N The distribution of the queue content will now converge to a limiting distribution as N 4 I and we can now meaningfully de ne upper and lower queue levels, xL N and xU N , respectively, corresponding to where this distribution has an approximate Weibullian behavior The lower level xL N will converge to a xed value with increasing N , corresponding simply to the beginning of the limiting Weibullian tail (as judged by the precision criterion) Here the essential fact to know is simply this limiting value, although details on the rate of convergence would also be useful The upper level xU N , however, will diverge with N and it is essential to have good estimates of xU N .

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