What This Document Is
This is a detailed exploration of 2<sup>k</sup> factorial designs, a core concept within the field of Computer Systems Analysis. It delves into the methodology behind systematically investigating the impact of multiple factors on a system’s performance. The material originates from CSE 567M at Washington University in St. Louis and represents a focused study of experimental design techniques used to optimize complex systems. It’s a technical resource intended for students and professionals seeking a deeper understanding of performance evaluation and analysis.
Why This Document Matters
This resource is invaluable for anyone involved in performance modeling, system design, or experimental research. It’s particularly beneficial for students taking advanced courses in computer systems, performance analysis, or experimental design. Professionals working on system optimization, bottleneck identification, or capacity planning will also find this material highly relevant. Understanding factorial designs allows for efficient and effective experimentation, leading to data-driven decisions and improved system performance. It’s most useful when you need to understand how several independent variables influence a specific outcome.
Common Limitations or Challenges
This document focuses specifically on 2<sup>k</sup> factorial designs and their application to computer systems. It assumes a foundational understanding of statistical concepts and experimental methodology. It does *not* provide a comprehensive introduction to all experimental design techniques, nor does it cover the practical implementation of these designs using specific software tools. The material emphasizes the theoretical underpinnings and analytical methods, and doesn’t offer ready-made solutions for all possible scenarios. It also highlights conditions where the effectiveness of this approach may be limited.
What This Document Provides
* A focused examination of 2<sup>k</sup> factorial designs, outlining their strengths and weaknesses.
* A discussion of how to model system performance using these designs.
* Methods for computing the effects of individual factors and their interactions.
* Techniques for allocating variation to understand the importance of each factor.
* Illustrative examples demonstrating the application of these designs to real-world computer systems problems, such as memory and cache configurations and interconnection networks.
* A detailed derivation of the underlying mathematical principles.
