Relay-Version: version B 2.10 5/3/83; site utzoo.UUCP Path: utzoo!watmath!clyde!cbatt!ucbvax!sdcsvax!darrell From: darrell@sdcsvax.UUCP Newsgroups: mod.os Subject: Re: Performance analysis of computer systems Message-ID: <2614@sdcsvax.UCSD.EDU> Date: Tue, 27-Jan-87 13:04:05 EST Article-I.D.: sdcsvax.2614 Posted: Tue Jan 27 13:04:05 1987 Date-Received: Wed, 28-Jan-87 21:51:27 EST Sender: darrell@sdcsvax.UCSD.EDU Organization: NASA Ames Research Center, Mountain View, CA Lines: 28 Approved: mod-os@sdcsvax.uucp -- The method I use for performance analysis depends heavily on the problem being investigated. Most of my work is in measurement and tuning of operating systems, so I usually start by instrumenting the system of interest and then performing statistical analysis on the results. [Could you explain how you "instrument" the system? -DL] This usually provides only a rough sketch of the system's behavior, because measurement interfers with behavior, and because instrumentation doesn't uncover all of the behavior. Once I have this rough sketch, I propose a series of experiments aimed at highlighting the behavior I'm interested in. I do this using the scientific method: propose a hypothesis explaining some behavior of the system, formulate an experiment which can disprove the hypothesis, perform the experiment, and evaluate the results. The experiments can consist of modifications to the measurements being made, applications of analytic models, stochastic or event driven model, and performance of workload benchmarks; as appropriate. In a recently completed analysis of the UniCos scheduler on the Cray 2, I have used all but stochastic models. My personal preferences for analysis tools are SPSS and SLAMII, not because they are inherently better than other tools, but because I am familiar with them and they are (usually) readily available. --