U. Glaeser

Jozo J. Dujmović
San Francisco State University
Daniel Tomasevich
San Francisco State University
Ming Au-Yeung
San Francisco State University
Lizy Kurian John
University of Texas
Eric Rotenberg
North Carolina State University
8.1 Measurement and Modeling of Disk
Subsystem Performance
© 2002 by CRC Press LLC
8
Performance Evaluation
8.1 Measurement and Modeling of Disk
Subsystem Performance
Introduction • Description Errors and Prediction Errors of
Disk Subsystem Models • A Simple Acceleration/Deceleration
Model of Disk Access Time • A Fixed Maximum Velocity
Model of Seek Time • Numerical Computation of the Average
Seek Time • A Simple Model of Cached Disk Access
Time • Disk Access Optimization Model • Disk Service Time
Model • Disk Subsystem Benchmark Workload • MVA
Models and Their Limitations • Experimental Results for
LDMVA Model of a Disk Subsystem with Access
Optimization • Experimental Results for LDMVA Model of a
Disk Subsystem with Caching and Access Optimization •
Predictive Power of Queuing Models • Conclusions
8.2 Performance Evaluation: Techniques, Tools,
and Benchmarks
Introduction • Performance Measurement • Performance
Modeling • Workloads and Benchmarks
8.3 Trace Caching and Trace Processors
Trace Cache and Trace Predictor: Efficient High-Bandwidth
Instruction Fetching • Trace Processor: Efficient
High-Bandwidth Instruction Execution • Summary via
Analogy
Jozo J. Dujmovi ć, Daniel Tomasevich, and Ming Au-Yeung
Introduction
In queuing theory literature, many models describe the dynamic behavior of computer systems. Good sources
of such information (e.g. [5,7]) usually include stochastic models based on birth-death formulas, the convolution
algorithm [2], load independent and load dependent mean value analysis (MVA) models [10], and
BCMP networks [1]. Theoretical queuing models presented in computer literature easily explain phenomena
such as bottlenecks, saturation, resource utilization, etc.; however, it is very difficult to find sources that show
a second level of modeling, which focuses on the ability of models to also achieve good numerical accuracy
when modeling real computer systems running real workloads. Although the phenomenology is important
in the classroom, it is the numerical accuracy that counts in engineering practice. The usual task of performance
analysts is to measure system performance and then derive models that can describe and predict the
behavior of analyzed systems with reasonable accuracy. Those who try to model the dynamic behavior of
real computer systems running real workloads frequently find this to be a difficult task.