What This Document Is
This resource is a focused guide within the Computer Systems Analysis (CSE 567M) course at Washington University in St. Louis. It delves into the critical area of performance evaluation, specifically addressing frequent pitfalls encountered when analyzing and assessing computer systems. The material is designed to help students develop a rigorous and systematic approach to understanding system behavior, moving beyond simply observing results to truly interpreting their meaning. It’s geared towards those seeking to build a strong foundation in the methodologies used to validate and improve system designs.
Why This Document Matters
This guide is invaluable for students and professionals involved in system design, performance engineering, and capacity planning. If you’re tasked with evaluating the effectiveness of a new system, comparing different architectural choices, or identifying bottlenecks in an existing infrastructure, this resource will provide a framework for avoiding common analytical errors. It’s particularly useful when you need to justify design decisions with data-driven insights and ensure the reliability of your conclusions. Understanding these potential mistakes *before* conducting an evaluation can save significant time and resources.
Common Limitations or Challenges
This resource focuses on identifying and preventing errors in the *process* of performance evaluation. It does not offer specific solutions to performance problems, nor does it provide detailed implementations of evaluation tools or techniques. It also doesn’t cover the specifics of any particular system or technology; instead, it presents broadly applicable principles. It assumes a foundational understanding of performance metrics and evaluation methodologies.
What This Document Provides
* A comprehensive overview of frequently made mistakes in system evaluation.
* A structured checklist to guide a systematic approach to performance analysis.
* A step-by-step framework for conducting thorough performance evaluations.
* A detailed case study illustrating the application of these principles to a real-world scenario involving remote procedure calls and data transmission.
* Identification of key parameters and factors influencing system performance.
* Guidance on appropriate workload selection and experimental design.
