Traditional monitoring relies on fixed thresholds: if CPU usage goes above 90 percent, raise an alert. This is simple and easy to understand, but it has two problems. First, false alarms pile up and the team gradually starts ignoring them. Second, many failures do not begin by crossing a threshold; they begin with a slow, unusual change in behavior that no threshold catches.
What is AIOps?
AIOps means applying machine learning and statistical methods to IT operations. The core idea is that the system learns from past data what “normal” looks like for each service at each hour and day, and reports deviations from it. For example, network traffic at 10 a.m. on a workday differs from 2 a.m. on a weekend; a fixed threshold cannot tell the difference, but a pattern-based model can.
Where does it really help?
- Anomaly detection: An unusual rise in outbound traffic at midnight, a gradual increase in a service’s response time, or a sudden jump in failed logins.
- Trend prediction: Estimating when, at the current rate, a server disk or backup repository will fill up. Some monitoring tools, such as Zabbix, have trend-forecasting functions that do this without much complexity.
- Alert correlation: When a switch fails, dozens of alerts arrive from the servers behind it. AIOps can group them into a single root event.
- Log analysis: Categorizing large volumes of logs and highlighting new patterns that have not been seen before.
Prerequisite: good data
No model gets good results from bad data. Before any AIOps tool, strengthen these foundations:
- Regular collection of metrics from devices via SNMP or an agent, and of logs via Syslog, in one central place.
- Clock synchronization on all devices with NTP; without it, comparing events is misleading.
- Enough retained history, at least several weeks, so daily and weekly patterns can be learned.
- An up-to-date asset inventory, so you know which service and which internal customer each alert relates to.
Do not limit monitoring to “up or down.” Server hardware health from a management console such as HP iLO, the results of backup jobs, and the arrival of database backup files should all be part of your monitoring data.
Limitations and risks
- Learning period: In the first weeks the model does not yet know normal behavior and may produce many false alerts.
- Treating a problem as normal: If a problem already exists, the model may learn it as part of normal behavior.
- Seasonal patterns: Month-end or year-end closing changes system behavior and can trigger false alerts.
- Black box: Some tools do not clearly explain why they raised an alert, so the team cannot trust them.
- Cost and complexity: Full AIOps platforms may be more than a small company needs.
Where to start?
You do not need to buy a complex platform on day one. Start with three simple steps: remove or fix repetitive alerts that lead to no action, set up trend prediction for disk and backup repository capacity, and define a baseline of normal behavior for a few critical services. Every alert should also have an owner and a defined action.
Checklist
- Metrics and logs are collected centrally.
- All device clocks are synchronized with NTP.
- Enough history is kept to learn patterns.
- False alerts are reviewed and removed periodically.
- Trend prediction is enabled for disks and backup repositories.
- Every alert has an owner and a defined procedure.
ITPlus724 engineers can set up monitoring for your company’s network, servers, hardware and backups so problems are seen before they turn into outages.