Online metrics prediction in monitoring systems
Matthieu Caneill, Noël De Palma, Ali Aït-Bachir, Bastien Dine, Rachid Mokhtari, Yagmur Gizem Cinar · 2018
Monitoring thousands of machines and services in a datacenter produces a lot of time series points, giving a general idea of the health of a cluster. However, there is a lack of tools to further exploit this data, for instance for prediction purposes. We propose to apply linear regression algorithms to predict the future behavior of monitored systems and anticipate downtimes, giving system administrators the information they need ahead of the problems arising. This problem is quite challenging when dealing with a high number of monitoring metrics, given our three main constraints: a low number of false positives (thus blacklisting volatile metrics), a high availability (due to the nature of monitoring systems), and a good scalability. We implemented and evaluated such a system using production metrics from Coservit, a company specialized in infrastructure monitoring. The results we obtained are promising: sub-second latency per predicted metric per CPU core, for the entire end-to-end process. This latency is constant when scaling the system up to 125 cores on 4 machines dedicated for monitoring predictions, and the performances don't decrease with time: during 15 minutes, it is able to handle more than 100 000 monitoring metrics.