Jozo J. Dujmovic, Daniel Tomasevich, Ming Au-Yeung San Francisco State University
This paper presents techniques for measurement and analytic modeling of disk subsystem performance. We measure disk subsystem performance using benchmark programs and develop analytic models of measured systems. The models are calibrated and used as tools for performance prediction. Our goal is to show problems related to practical modeling of real life computer systems and limitations of some traditional modeling techniques. We present models of load-dependent disks, disk cache, and load-dependent Mean Value Analysis models having high predictive power. Our results show that real systems include highly nonlinear behavior caused by disk optimization algorithms and by caching, and cannot be satisfactorily modeled using the traditional load-independent Mean Value Analysis. We performed experiments for a variety of operating systems including Windows NT. The development of load-dependent models, on the other hand, is not simple and requires a substantial calibration effort. We developed a new quantitative indicator for evaluation of predictive power of our analytic models.
The results of this research have been obtained in the Experimental CS Lab developed using the NSF grant ILI DUE-9751724. Without this grant our experimental work would not be possible.