Statistical Analysis is Valuable for Understanding

In System Administration – and many other areas – statistics can assist us in understanding the real meaning hidden in data. There are many places that statistical data can be gathered and analyzed, including from sar data and custom designed scripts in Perl or Ruby or Java.

How about the number of processes, when they are started, when they finish, and how much processor time they take over the length of time they operate? Programs like HP’s Performance Agent (now included in most HP-UX operating environments) and SGI’s fabulous Performance CoPilot can help here. In fact, products like these (and PCP in particular) can gather incredibly valuable sorts of data. For example, how much time does each disk spend above a certain amount of writing, and when? How much time does each CPU spend above 80% utilization and when?

Using statistical data from a system could, with the proper programming, be fed back into a learning neural network or a bayesian network and provide a method of providing alarms for stastically unlikely events.

There are other areas where statistical analysis can provide useful data than just performance. How about measuring the difference between a standard image and a golden image based on packages used? How about analyzing the number of users that use a system, when they use it, and for how long? (Side note: I had a system once that had 20 or 30 users that each used the system heavily for one straight week or two in every six months… managing password aging was a nightmare…)

There are many places for analyzing a system and providing statistical data; this capability, however, has not been utilized appropriately. So what are you waiting for?

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