Anomaly Detection for Monitoring

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Conclusions If you are like most of our friends in the DevOps and web operations communities, you probably picked up this book because you’ve been hearing a lot about anomaly detection in the last few years, and you’re intrigued by it. In addition to the previouslymentioned goal of making assumptions explicit, we hope to be able to achieve a number of outcomes in this book. • We want to help orient you to the subject and the landscape in general. We want you to have a frame of reference for thinking about anomaly detection, so you can make your own decisions. • We want to help you understand how to assess not only the meaning of the answers you get from anomaly detection algorithms, but how trustworthy the answers might be. • We want to teach you some things that you can actually apply to your own systems and your own problems. We don’t want this to be just a bunch of theory. We want you to put it into practice. • We want your time spent reading this book to be useful beyond this book. We want you to be able to apply what you have learned to topics we don’t cover in this book. If you already know anything about anomaly detection, statistics, or any of the other things we cover in this book, you’re going to see that we omit or gloss over a lot of important information. That is inevitable. From prior experience, we have learned that it is better to help people form useful thought processes and mental models than to tell them what to think. As a result of this, we hope you will be able to combine the material in this book with your existing tools and skills to solve problems on your systems. By and large, we want you to get better at what you already do, and learn a new trick or two, rather than solving world hunger. If you ask, “what can I do that’s a little better than Nagios?” you’re on the right track. Anomaly detection is not a black and white topic. There is a lot of gray area, a lot of middle ground. Despite the complexity and richness of the subject matter, it is both fun and productive. And despite the difficulty, there is a lot of promise for applying it in practice. 6 | Chapter 1: Introduction
Somewhere between static thresholds and magic, there is a happy medium. In this book, we strive to help you find that balance, while avoiding some of the sharp edges. Conclusions | 7
Page 3 and 4: Anomaly Detection for Monitoring A
Page 5 and 6: Table of Contents Foreword. . . . .
Page 7: Foreword Monitoring is currently un
Page 10 and 11: tion” to anomaly detection is imp
Page 12 and 13: Why do we assume these things? Are
Page 17 and 18: CHAPTER 2 A Crash Course in Anomaly
Page 19 and 20: How can you achieve similar results
Page 21 and 22: cause most anomaly detection techni
Page 23 and 24: CHAPTER 3 Modeling and Predicting A
Page 25 and 26: (say, the size of the drill bit), a
Page 27 and 28: lies. To fix this problem, the cont
Page 29 and 30: ally decaying window. This is made
Page 31 and 32: ack again, meaning that they smooth
Page 33 and 34: emains consistent across all of the
Page 35 and 36: Evaluating Predictions One of the m
Page 37 and 38: No. You’ve stumbled into statisti
Page 39: As an aside, there’s a rumor goin
Page 42 and 43: Dealing with Trend Trends break mod
Page 44 and 45: pletely out of phase with the seaso
Page 46 and 47: mon situations. You can probably gu
Page 49 and 50: CHAPTER 5 Practical Anomaly Detecti
Page 51 and 52: If you can get close to that, you m
Page 53 and 54: We previously discussed how frequen
Page 55 and 56: First, observe how odd this metric
Page 57 and 58: After differencing, it looks like w
Page 59 and 60: Perhaps, instead of differencing, w
Page 61 and 62: CHAPTER 6 The Broader Landscape As
Page 63 and 64: We could apply a EWMA control chart
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figure out how unlikely it was for
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predict or find structure in data w
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Graphite and RRDTool Graphite and R
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APPENDIX A Appendix Code Control Ch
Page 73 and 74:
var ma = new movingAverage(0.5); ma
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Acknowledgments We’d like to than
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