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Unless you are looking at a directory with a very large number of files in it, ls -la is quite a good way to use the ls command. If there is anything unexpected about the sizes or ownerships of the files, you will be able to notice it, and dot files will be displayed. The -t option to ls can be very useful. Suppose, for example, you have recently done something that has caused a file to be created in a large directory, but you have forgotten or don t know the name of the file. The ls -lat command lists the files in order of modification time, so the new or newly modified files will be at the top of the listing. If you want to get a full recursive listing of all files beneath a particular directory, the command ls -laR is what you want. This can be particularly useful if you are doing some detective work to find out (for example) what is being changed when you make some change to a system using a graphical or other configuration tool, but you don t know which file(s) are being changed. If you do an ls -laR before and after making the change, writing the output to a file, you can then compare the two files and work out what has happened.

Figure 11.4 The weekly average of privileged (root) processes shows a constant daily pulse, steadily on week days. During weekends there is far less activity, but wider variance. This might be explained by assuming that root process activity is dominated by service requests from users

(Some change to the system.)

20

Here we have made some change to the system at the step marked (Some change to system.), before and after which we created separate files containing a listing of the files existing at that time. Using diff, you can see what has changed. Very occasionally it can be useful to know that ls -i shows you the inodes to which the files are attached in the underlying filesystem. In particular, this can help you to understand hard links:

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Figure 11.5 The daily average of non-privileged (user) processes shows an indisputable, strong daily rhythm. The variation of the graph is now greater than the uncertainty reflected in the error bars

user@bible:~/directory> touch afile user@bible:~/directory> touch bfile user@bible:~/directory> ln afile cfile user@bible:~/directory> ls -la total 8 drwxr-xr-x 2 user users 4096 2004-06-20 drwxr-xr-x 32 user users 4096 2004-06-20 -rw-r--r-2 user users 0 2004-06-20 -rw-r--r-1 user users 0 2004-06-20 -rw-r--r-2 user users 0 2004-06-20

10:44 10:31 10:44 10:44 10:44

Maximum percentage CPU used in processes: this is an experimental measure which characterizes the most CPU expensive process running on the host at a given moment. The significance of this result is not clear. It seems to have a marginally periodic behaviour, but basically inconclusive. The error bars are much larger than the variation of the average, but the magnitude of the errors also increases with the increasing average, thus; while for all intents and purposes this measure's average must be considered irrelevant, a weak signal can be surmised. The peak value of the data might be important however, since a high max-cpu task will significantly load the system. See Figures 11.7 and 11.8.

user@bible:~/directory> ls total 0 1259203 -rw-r--r-- 2 user 1259203 -rw-r--r-- 2 user 1259206 -rw-r--r-- 1 user

The design of fuzzy controllers is one of the largest application areas of fuzzy set theory. Where fuzzy logic is frequently described as computing with words rather than numbers, fuzzy control is described as control with sentences rather than equations. Thus, instead of describing the control strategy in terms of differential equations, control is expressed as a set of linguistic rules. These linguistic rules are easier understood than systems of mathematical equations. The first application of fuzzy control comes from the work of Mamdani and Assilian in 1975, with their design of a fuzzy controller for a steam engine [Mamdani et al. 1975]. The objective of the controller was to maintain a constant speed by controlling the pressure on pistons, by adjusting the heat supplied to a boiler. Since then, a vast number of fuzzy controllers have been developed for consumer products and industrial processes. For example, fuzzy controllers have been developed for washing machines, video cameras, air conditioners, etc., while industrial applications include robot control, underground trains, hydro electrical power plants, cement kilns, etc. This chapter gives a short overview of fuzzy controllers. Section 20.1 discusses the components of such controllers, while Section 20.2 overviews some types of fuzzy controllers.

Weekly period Daily period Average type Expected entropy Marginal/none Marginal/none Peak value only High

-il|sort -n users 0 2004-06-20 10:44 afile users 0 2004-06-20 10:44 cfile users 0 2004-06-20 10:44 bfile

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