Some systems, applications and models I have known

Abstract

Being named recipient of the 2004 ACM Sigmetrics Achievement Award has done several things to me. It brought me surprise that I would be singled out from the many people who have made significant and sustained contributions to the field of performance evaluation. It also brought me deep appreciation for all the students and colleagues with whom I have worked and come to know as friends over the years. Finally, it has caused me to ponder and reminisce about many of the research projects and consulting studies in which I have participated.In this talk, I will describe various systems I have used and studied, various applications of interest, and various models that I, and others, have used to try to gain insights into the performance of systems. Some lessons of possible future relevance that emerge from this retrospective look at a wide variety of projects are the following:

Exact Answers Are Overrated -- While exact solutions of mathematical models are intellectually satisfying, they are often not needed in practice. Analytic Models Have a Role -- Analytic models can be used to obtain quick and inexpensive answers to performance questions in many situations where neither simulation nor experimentation are feasible. Assumptions Matter -- Subtle changes to the assumptions that underlie an analytic model can substantially alter the conclusions reached based on the model.
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我所知道的一些系统、应用程序和模型
被提名为2004年ACM Sigmetrics成就奖的获得者对我来说有很多好处。让我感到惊讶的是，在许多对绩效评估领域做出重大和持续贡献的人中，我被挑出来。这也让我对所有的学生和同事深表感谢，这些年来，我与他们一起工作，并成为朋友。最后，它让我对我参与的许多研究项目和咨询研究进行了思考和回忆。在这次演讲中，我将描述我使用和研究的各种系统，各种感兴趣的应用程序，以及我和其他人用来试图深入了解系统性能的各种模型。从对各种各样的项目的回顾中得出的一些可能与未来相关的教训如下:精确答案被高估了——虽然数学模型的精确解决方案在智力上令人满意，但在实践中往往不需要。解析模型有一个作用——解析模型可以用于在许多既不可行模拟也不可行实验的情况下获得性能问题的快速且廉价的答案。假设很重要——分析模型中假设的细微变化会极大地改变基于模型得出的结论。在考虑了所有的分析、模拟和实验方法之后，我建议大幅度提高计算机系统性能的最佳方法是:等三十年!
本文章由计算机程序翻译，如有差异，请以英文原文为准。