May 5, 2021. By Aleena Mathew
IT system monitoring became a fire fighting task with the up come of the digital era. IT operators had to monitor several IT resources and components to ensure the smooth run of the organization. Monitoring was the only solution at that time and, IT operators need to watch every element manually and continuously. They had to detect and identify if there are any unknown issues in the system. These were all time-consuming tasks and eventually added to the cost of operations. The number of KPIs to monitor increased drastically as the use of more applications came into the picture. This affected IT operations and business and, also the need for a change necessary. This is where Artificial Intelligence stepped in and took over the entire IT operation with AIOps.
The implementation of AI-based observability helped in resolving one of the main challenges faced by the IT organization. An entire shift from the monitoring phase to an AI-based observability system was made possible. AI-based observability enabled the systems to ingest every IT data including, KPI automatically. Based on this, the AI-based models deployed will automatically trigger alerts to unknown issues and events. The IT operators can automatically take proactive actions to resolve the problems that are occurring in the system. In this way, the end-user side is also not at stake as the system is capable of resolving the issues. Apart from that, AI-based observability is enabled in providing a unified method of monitoring and observing every IT resource and components. This unified monitoring helped the IT operators to get a clear picture of the entire system, in which they were able to manage and monitor every IT resource. Based on this observation, the IT operators could identify any unknown issues directly from AI-based observing tools.
The role of in Observability KPI Data Collection:
With the AI-based models, the process of data collection was made easy from the existing system and new system. KPI data collection was made possible from an existing system such as existing vendor tools or other open-source tools. This enabled the IT systems to collect and correlate every KPI from different and multiple sources. Also, along with the correlation of the existing system, the analysis of KPI data and identifying inference from them became more meaningful. IT teams were able to identify any unknown issues easily and proactively resolve them. Apart from that, direct collection of KPI data were made possible. Direct data collection where from OpenTelemtry source or cloud-native systems. This collection enabled correlating KPI data from existing and new systems together in which the IT operators were able to drive proper inference. With all these KPI data collections made possible, another feature of multivariate KPI analysis and anomaly detection is made possible with AI. Let's take a deeper look into that scenario.
Multivariate KPI Analytics:
IT operators faced a prominent challenge in correlating and understanding KPIs together. Monitoring individual business and IT system KPI and getting inference from them no longer benefited the IT operations. As the number of KPIs generated multifold, individual KPI analysis needed to stay back. That's where the concept of Multivariate KPI analytics came into the picture. In multivariate KPI analytics, the correlation of multiple KPI is made possible. In this way, IT operators can easily correlate multiple KPIs together and identify if there are any unknown issues or anomalies in the system. The implementation of correlating multiple KPIs together enabled correlating business and IT metrics together. This helped in providing a deeper analysis of the systems. Let's see this mechanism effectively with a use-case scenario.
Multivariate KPI Anomaly Detection:
Most organizations use multiple applications for user support. There are specific scenarios in which the applications can be overloaded with user transactions at a point. This can lead to a situation in which the database cannot process all these user transactions, i.e., all these transaction requests will come in a database query. The DB at a certain point will not handle the query, and eventually, some queries will move into a cached state. As the system's cache starts to increase, the system performance will start to be affected—the CPU load increases drastically, affecting the entire system performance. The end-user will only notice this as the system is hanging out or stuck. That's where multivariate KPI comes into play. Correlating these KPIs together, the system will identify the situation as an anomaly. As the cache of the system starts to increase, CPU load increases and, this will be identified as an anomaly by the AI-based models. Based on this detection, the IT team can proactively resolve the issue before affecting the end-user side.
AI-based multivariate KPI analytics and anomaly detection enable the IT team to correlate and manage different KPIs together proactively. This helps in correlating business and IT metrics together and providing a deep end-to-end analysis.
To learn more about Algomox AIOps, please visit our AIOps Platform Page.