Home > Quantifying Abnormal Behavior

Quantifying Abnormal Behavior

At Velocity last week, I spoke about how we quantify abnormality in a system’s time-series metrics cheaply, in realtime, at high frequency. Note that this is not the same thing as our Adaptive Fault Detection algorithm. Our abnormality algorithm is one of the low-level building blocks of the adaptive fault detection algorithm. But as I pointed out in the talk, if you look at a system’s metrics in short time intervals, you will find abnormalities constantly. That’s why abnormality is a blunt instrument, not good enough to significantly reduce false alarms. If you alert on abnormalities, you’ll get a lot of spam, just like you will with thresholds on a metric. Still, abnormality is at least a place to start, right? In the progression towards true fault detection, you can think of it this way: a fault is more specific than an abnormality, and an abnormality is more specific than a threshold being crossed. This assumes that you agree with our definition of a fault, which is defined in terms of the system not getting its assigned work done.
Baron Schwartz
Baron is a performance and scalability expert who participates in various database, open-source, and distributed systems communities. He has helped build and scale many large,…
Read more

Tweets

SolarWinds's Twitter avatar
SolarWinds
@solarwinds

Great APM isn’t in a galaxy far, far away. In fact, AppOptics is TopRated! t.co/QxxvNUgzpL

SolarWinds's Twitter avatar
SolarWinds
@solarwinds

ICYMI: This fifth and final piece to the CMMI overview covers Level 5 and the advantage of business agility from a… t.co/nuDX46mbj2

SolarWinds's Twitter avatar
SolarWinds
@solarwinds

It pays to be proactive instead of reactive during incident management. And there’s wealth in setting up time-based… t.co/fZZRSVsJNk