Relay-Version: version B 2.10 5/3/83; site utzoo.UUCP Path: utzoo!mnetor!seismo!mcnc!ece-csc!ncrcae!ncr-sd!sdcsvax!darrell From: fouts@orville%ames.arpa (Marty Fouts) Newsgroups: mod.os Subject: Submission for mod-os Message-ID: <2687@sdcsvax.UCSD.EDU> Date: Tue, 3-Feb-87 18:08:24 EST Article-I.D.: sdcsvax.2687 Posted: Tue Feb 3 18:08:24 1987 Date-Received: Sat, 7-Feb-87 09:35:55 EST Sender: darrell@sdcsvax.UCSD.EDU Organization: NASA Ames Research Center, Mountain View, CA Lines: 27 Approved: mod-os@sdcsvax.uucp Direct measurement is very useful, as Gene has pointed out, but suffers from drawbacks: 1) measurement interfers with the system being measured. 2) measurement is subject to sampling frequency related distortion. 3) measurement can miss transients. 4) measurement still requires analysis and interpretation. 5) Some times you need to know if it will work before you can build it. In fact, measurement by itself doesn't solve most of Gene's concerns. It is easy to cook a set of measurements to obtain the desired results. After all, many vendor supplied benchmarks are merely cooked measurements. You do trade off in reality. You ignore events that you can't process quickly enough, you ignore data you don't understand, and you ignore events that you can't measure adequately. Direct measurement has as many drawbacks as modeling. Measurement is only the first step in understanding. Building models to predict future behavior is the second. Comparing the results of models to real measurements is the third. You only understand something if you can predict its behavior and demonstrate the validity of your predictions. In the context of computer performance analysis, a set of measured results will frequently lead to ambiguous interpretation. It is sometimes not possible to find a measurement which will distinguish between the alternatives without first producing at least an analytic model of the system.