AI infrastructure monitoring (AIM) is a function within IT infrastructure management that makes use of artificial intelligence and machine learning algorithms to manage and monitor an organization's IT infrastructure.
An AI infrastructure monitoring tool is capable of analyzing large volumes of data from different ends of the network. It goes through logs, metrics, and events to identify patterns and anomalies quickly that could signify a potential issue in the infrastructure. This translates into a predictive approach to infrastructure management where potential issues are noticed well beforehand and resolved in time before impacting the network.
Real-time anomaly detection: AI (Artificial Intelligence) Infrastructure Monitoring solutions make use of AI to automatically identify anomalies in real-time. Let's consider an infrastructure, where servers have a bench-marked pattern of resource usage. Whenever the server's load pattern spikes beyond normal levels, real-time anomaly detection ensures that an alert is raised to the IT admin. The anomaly is quickly detected and an investigation can begin as to what caused the spike. Real-time anomaly detection provides IT professionals a head start for resolving the issue before it can bring downtime or performance degradation in the IT infrastructure. Moreover, real-time anomaly detection recognizes security vulnerabilities, letting IT admins take proactive steps to preserve the infrastructure from external threats.
Predictive analytics: Historical data is used to predict future issues and failures in the IT infrastructure. For example, an IT admin can utilize multiple reports to ascertain the resource utilization trend of storage system. The historical data will outline how the storage has been filling up over time and when it will need increased capacity. An IT admin can effectively plan the capacity and proactively make improvements to the infrastructure, before storage space runs out and causes downtime. With predictive analytics, an IT admin can find out which devices are prone to hardware failure by analyzing historical data of similar devices or vendors. By understanding which type of devices or vendors are more likely to fail, an IT admin can take proactive steps to replace the component before it can lead to downtime in the infrastructure.
Root cause analysis: This feature in an AIM solution helps IT admins get to the bottom of an issue and identify the underlying cause. Knowing the root cause of an issue enables the IT team to make effective and targeted efforts to resolve the issue and prevent it from recurring. For example, a complaint is raised that the applications running on a server is experiencing slow response times. Infrastructure monitoring collects metrics like CPU utilization, memory usage, network traffic, and more. With the help of root cause analysis, the IT admin ascertains that the slower response time is due to high CPU utilization on the server. The application process could be consuming CPU resources quickly, causing a slowdown. An IT admin can now make steps to optimize the application to be more efficient or allocate more CPU resources to the server.
Workflow automation: Automation of routine tasks and processes using workflow automation options gives an IT admin more room to focus on higher-level tasks. In AI infrastructure management, you need to optimize the components constantly in the form of patches to ensure security and optimum performance. But manually executing the tweaks, server by server, can be a cumbersome and time-consuming task. Using workflow automation, an IT Infrastructure Monitoring solution can analyze each configuration, requirements, and execute the latest change accordingly. Workflow automation is also used in performance monitoring, report generation, and responding to alerts. Workflow automation, at the end of the day, frees up time for an IT admin to focus efforts more on long-term initiatives like improving the infrastructure's reliability, user experience, efficiency, and lower the overall costs.
The Workflows feature in OpManager Plus gives you multi-fold benefits that can elevate your infrastructure artificial intelligence infrastructure management. AI algorithms detect anomalies or events in the IT environment promptly. The incident management process is the biggest beneficiary, as each ticket that comes up pertaining to a network bottleneck or an anomaly is automatically assigned to the specialized personnel. Automated incident response, quick remediation actions, and escalation to appropriate teams all contribute to creating a well-oiled incident management process that will drastically reduce downtime or other issues.
OpManager Plus continuously analyzes infrastructure performance metrics, logs, and events. The real-time analysis enables proactive alerts and notifications, informing IT admins of anomalies and potential issues in the infrastructure. Automated alerts include threshold-based alerts and event correlation alerts. A threshold-based alert can be set up to trigger when a specific performance threshold is breached, like disk utilization or CPU temperature. Event correlation alerts can be configured to look for patterns of events that may signify an issue, like multiple failed login attempts to the network.
The historical data that is collected by OpManager Plus over the course of end-to-end infrastructure monitoring, directly contributes to improving the accuracy of forecast reports. Forecast reports look into the future with trends and patterns to show you how the infrastructure will fare. With forecast reports, IT admins can take proactive measures to prevent downtime, perform capacity planning, and make overall better strategic decisions.
Root cause analysis is an integral tool to detect the underlying causes of an issue and take corrective actions before it affects your infrastructure.
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