As compared with traditional monitoring and management solutions, AIOps provides insights instead of the human user looking at data and then sorting out what is going on.
AIOps is displacing traditional approaches to IT management that rely solely on monitoring and alerting. The move to AIOps is needed because of the complexity and dynamic nature of modern infrastructures.
Until recently, network infrastructures were relatively static. Physical boundaries separated the corporate network that contained most end-user applications, data, and services. Thus, from a network perspective, if the network devices were up and pushing packets, relatively little added visibility was required. SNMP, ping, traceroute, and Syslog reporting was all that was needed.
The use of cloud-based resources (applications, compute power, infrastructure, and more) makes network management more challenging. Visibility gaps in network monitoring and alerting tools arise with networks now stretching into third-party managed infrastructure-as-a-service (IaaS) clouds and apps/data moving into platform-as-a-service (PaaS) and SaaS environments.
While more monitoring and alerting capabilities are great, they can add to the workload of an already busy network administrator. That is why the industry is undergoing a shift away from separate network, application, and device monitoring tools towards artificial intelligence (AI) for IT operations (AIOps).
Benefits of AIOps
An AIOps platform should automate anomaly detection and cover a wide range of observability data. At a base level, an AIOps solution should automatically discover the relationships between status data and the business outcome. As compared with traditional monitoring and management solutions, AIOps should provide insights instead of the human user looking at data and then sorting out what is going on. A suitable platform should tell an IT manager that there is something that needs attention.
A solution that supports those capabilities can deliver a number of benefits, including:
- Reduced downtime: AIOps should help guarantee the availability of services. It should catch more issues and catch them earlier, allowing changes to be made before there is an impact.
- Reduced workload: A great benefit of the advanced correlation available with AIOps is a radical reduction in false alarms and elimination of noise. The wasted time chasing down pointless alerts kills SRE/DevOps/ITOps productivity. An AIOps solution should deliver a significant reduction in false alerts.
- Reduced cost of ownership: With rules-based systems, IT staff was constantly tweaking the configuration of their monitoring systems. Every single change in an application infrastructure potentially required a change to the monitoring systems. AIOps removes that dependency. It is built with continuous change in mind.
A last word
AIOps platforms combine traditional monitoring tools with streaming telemetry and analyze all of it using AI. AI analyzes each data source and correlates multiple anomalies to automate the identification of problems while also providing detailed information on how to fix the issue. Thus, if an AIOps platform is properly implemented, not only does it provide more visibility into potential problems. It also eliminates many manual troubleshooting and remediation tasks.