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The quantifiers which can be usefully measured or recorded on operating systems are the variables which can be used to provide quantitative support for or against a hypothesis about system behaviour. System auditing functionality can be used to record just about every operation which passes through the kernel of an operating system, but most hosts do not perform system auditing because of the huge negative effect it has on performance. Here we consider only metrics which do not require extensive auditing beyond what is normally available. Operating system metrics are normally used for operating system performance tuning. System performance tuning requires data about the efficiency of an operating system. This is not necessarily compatible with the kinds of measurement required for evaluating the effectiveness of a system administration model. System administration is concerned with maintaining resource availability over time in a secure and fair manner. It is not about optimizing specific performance criteria. Operating system metrics fall into two main classes: current values and average values for stable and drifting variables, respectively. Current (immediate) values are not usually directly useful, unless the values are basically constant, since they seldom accurately reflect any changing property of an operating system adequately. They can be used for fluctuation analysis, however, over some coarse-graining period. An averaging procedure over some time interval is the main approach of interest. The Nyquist law for sampling of a continuous signal is that the sampling rate needs to be twice the rate of the fastest peak cycle in the data if one is to resolve the data accurately. This includes data which are intended for averaging since this rule is not about accuracy of resolution, but about the possible complete loss of data. The granularity required for measurement in current operating systems is summarized in the following table:

Once an RPM has been installed, you may need to remove it from the system for one reason or another. As it does when installing packages, RPM will check the dependencies of the package you wish to remove. This check of dependencies is as important as checking package dependency during installation because you could end up deleting files that another RPM package depends on. To remove packages, you need to erase (-e) the package from the system. As with installation of packages, RPM checks to see if any other RPM packages on the system depend on the package you wish to remove. If a package does depend on it, the removal will fail. See Listing 12-6 for an example.

0 5 sees 10 30 secs 10 30 mins Hourly average Daily average Weekly average Fine grain work For peak measurement For coarse grain work Software activity User activity User activity

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bible:/media/dvd/suse/i586 # rpm -e blackbox error: Failed dependencies: blackbox is needed by (installed) bbtools-2003.10.16-97

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In this example, you have been told that the package bb-tools depends on the Blackbox package we are trying to remove. To resolve this, you have two options, one good and one bad: The bad option, which can prove marginally useful during both installation and removal of packages, is to tell RPM to ignore package dependencies. This should never really be used when removing a package, because as in the case of bbtools, other packages will stop working. However, during package installation, you may know for certain that a dependency that RPM is aware of is in fact resolved by a source-compiled library you have installed, or are testing. To tell RPM to not check for dependencies, use the -nodeps parameter as well as the usual rpm -Uvh. The good option (you do want to be a good administrator, don t you ) is to remove the dependent package as well as the package you originally wanted to remove, as shown in the following:

Although kernel switching times are of the order of microseconds, this timescale is not relevant to users' perceptions of the system Inter-system cooperating requires many context switch cycles and I/O waits These compound themselves into intervals of the order of seconds in practice Users themselves spend long periods of time idle, ie not interacting with the system on an immediate basis An interval of seconds is therefore sufficient Peaks of activity can happen quickly by user perceptions, but they often last for protracted periods, thus ten to thirty seconds is appropriate here Coarse grained behaviour requires lower resolution, but as long as one is looking for peaks, a faster rate of sampling will always include the lower rate There is also the issue of how quickly the data can be collected.

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