Real-Time Predictive Analytics for Web Servers
We recently made the decision to move to Zabbix to monitor one of our busy production Apache web servers. One of the things we need to do in the future is try to predict system outages and take corrective actions before the system actually goes down.
For example, recently a busy server experenced an outage that appeared to be caused by either a kernel bug or a cyberattack based on the Treason Uncloaked! TCP issue. The events leading up to the outage were so severe that our server logs and system stats halted before the outage occurred. The situation was complex and we still don’t know exactly what caused the problem.
This is a good opportunity for us to experiment with some real-time predictive analytics. So, after we get our agents and logfile monitoring extentions configured to gather the required event data, such as logfile entries, cpu stats, open file descriptor stats, open sockets, spiders on site stats, etc., we plan to move to the next step.
Our vision for the next step is to feed production web server and network events into either a neural or Bayesian network and build a baseline of normal patterns and then see if we can use open source (free) predictive analytics to help us prevent future outages by alerting, ahead of time, to intervene.