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Most likely, whatever system problem you are trying to solve or information you are trying to collect has been previously solved or collected by someone else. Before you spend a lot of time writing a homegrown single-use application, do a web search for something like it. Usually it is much easier to customize an existing application for your purposes than it is to write something from scratch. The customization process is also much less prone to error. The following are some examples of useful public domain software: Perl. A very useful programming language that was designed by and for system administrators. It runs on just about every major operating system. Tripwire. A Unix utility that watches a list of key system files and notifies the administrators if the files are altered. Top and wintop. These utilities return as output a list of the processes running on the local system, arranged and sorted any number of ways. Top is the Unix version; wintop is the Windows version. Ntp. A cross-platform clock-synchronizing utility. All the systems on your network synchronize their clocks with a preconfigured time server, or with an external server such as an atomic clock located out on the Web. If you do find it necessary to create your own utilities, use the commandline interface, rather than a Windows-based one. It is not hard to imagine a scenario where the person who dials into the server to fix it only has access to a slow line on a dumb terminal from very far away. A command line interface makes the repair process much simpler; no specialized hardware is required.



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There is a strong need therefore to monitor the value of scaling parameters over time to distinguish between these possibilities, and to test if constancy of scaling can be concluded or not. A basic approach is to split a data set into a number m of adjacent blocks, and to examine the corresponding logscale diagrams. If alignment is found ^ in each over roughly the same range of scales, then separate estimations a m of the scaling exponent could be performed for each block. A null hypothesis might be that the (unknown) values a m share a common value. The dif culty is how to combine these estimates in a well-de ned statistical test, given that the problematic statistical nature of scaling processes would in general imply strong correlations between the ^ a m . Because of the quasi-decorrelation in the wavelet plane, however, it can be ^ shown [77] that the a m can be treated as almost independent, and moreover that they are approximately normally distributed with known variances. The dif cult problem of the constancy of the scaling exponent can thereby be reduced to a simple





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Ci cells=frame for VC i, with the rest of the cells, if any, scheduled on a nonguaranteed basis Ci is based on (i) marginal distribution of #cells=frame, (ii) maximum acceptable probability of cell loss in a frame, and (iii) the equivalent bandwidth of all VCs in this jitter class The Ci are computed by the equivalentbandwidth unit described in Item 4 below 3 An Active Cell-Discard Unit If there are excess cells left over from a frame at the multiplexer after the corresponding frame time is over, it is likely that this is due to persistence in the arrival process as suggested by the solid lines in Fig 191 These cells are likely to cause increased delay for cells in successive frames.

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Regardless of the type of system or the method you choose, it is imperative that all systems in a cluster be time-synchronized. Applications and operating systems can get confused when a fundamental assumption is violated: that time always flows in the same direction, forward, at the same rate of one second per second. If systems are out of time-sync, it is possible for one system to create a file with a timestamp that a second system regards as being somewhere in the future. Results can be most inconsistent and confusing. The Unix make utility, for example, will not behave properly when a file s timestamp occurs in the future.

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