Anomaly Detection for Monitoring

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more interested in using a cloud-based anomaly detection service. For example, companies like Ruxit, VividCortex, AppDynamics, and other companies in the Application Performance Management (APM) space offer anomaly detection services of some kind, often under the rubric of “baselining” or something similar. The benefits of using a cloud service are that it’s often much easier to use and configure, and providers usually have rich integration into notification and alerting systems. Anomaly detection services might also offer better diagnostic tools than those you’ll build yourself, especially if they can provide contextual information. On the other hand, one downside of cloud-based services is that because it’s difficult to build a solution that works for everything, it may not work as well as something you could build yourself. 62 | Chapter 0: The Broader Landscape
APPENDIX A Appendix Code Control Chart Windows Moving Window function fixedWindow(size) { this.name = 'window'; this.ready = false; this.points = []; this.total = 0; this.sos = 0; this.push = function(newValue) { if (this.points.length == size) { var removed = this.points.shift(); this.total -= removed; this.sos -= removed*removed; } this.total += newValue; this.sos += newValue*newValue; this.points.push(newValue); this.ready = (this.points.length == size); } this.mean = function() { if (this.points.length == 0) { return 0; } return this.total / this.points.length; } 63
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Anomaly Detection for Monitoring A
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Table of Contents Foreword. . . . .
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Foreword Monitoring is currently un
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tion” to anomaly detection is imp
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Why do we assume these things? Are
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Conclusions If you are like most of
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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
Page 65 and 66: figure out how unlikely it was for
Page 67 and 68: predict or find structure in data w
Page 69: Graphite and RRDTool Graphite and R
Page 73 and 74: var ma = new movingAverage(0.5); ma
Page 75: Acknowledgments We’d like to than
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