PV Monitoring Based on Linear Regression

Thursday, June 07, 2018 - 4:15 pm4:40 pm

Wang Bo, Baidu

Abstract: 

PV (Page View) curve is one of the most important curves for SREs. Every significant drop on the curve is regarded as an incident. Therefore, SREs are badly in need of a good anomaly detection algorithm.

Because PV fluctuates during day and night, the detection heavily depends on its expected values. Moving average is a naïve method to generate the expected values. It suffers from two reasons. First, it lags behind the actual trend, which will miss the drop on a rise trend. Second, it cannot easily differentiate between the drop and the recovery after a rise. Advanced methods such as exponential smoothing also have their own shortcomings. When PVs are large, the local fluctuations of the curve are relatively small, rendering a smooth curve. This inspired us to apply linear regression to generate the expected value. But linear regression is susceptible to abnormal values.

In this talk, we will present a method based on robust linear regression to compute expected values. This method is able to resist the impact of anomalies. Moreover, we will also introduce a statistical hypothesis testing method to detect anomalies, eliminating the need to set different thresholds at different time in simple methods.

Wang Bo, Baidu

I am a Senior Software Engineer at Baidu. I am mainly engaged in operation data analysis, including the time-series anomaly detection, fault diagnosis.

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Presentation Audio

BibTeX
@conference {214971,
author = {Wang Bo},
title = {{PV} Monitoring Based on Linear Regression},
year = {2018},
publisher = {{USENIX} Association},
}