AIOps is a combination of artificial intelligence (AI) and machine learning (ML) technologies incorporated into the management of your IT infrastructure. In 2017, Gartner coined the term AIOps to include platforms that "analyze telemetry and events, and identify meaningful patterns that provide insights to support proactive responses. With the help of observed telemetry, AIOps help teams collaborate better, detect issues faster, and resolve them quickly, before the end user is impacted.
Prediction is better than remediation. An AIOps-powered network management software can collect data from multiple IT sources, process that data using AI and ML technologies, and provide solutions for you. Ultimately, the goal with AIOps platforms is to empower ITOps professionals with the insights they can use to detect issues earlier and resolve them faster. An AIOPs-enabled network management tool (such as OpManager Plus) analyzes the observable telemetry obtained from your IT infrastructure and incorporates its advanced ML and AI-based algorithms to make predictions and identify probable errors. This facilitates SREs and DevOps team perform seamlessly and gives an upper hand in fixing a glitch before it affects the end users.
Let's understand the backend of an AI-enabled network management solution from scratch. An AIOps platform operates on a three tier architecture.
In the ingestion phase, all relevant historical and real-time data collected from observability telemetry is analyzed for any shortcomings or problematic patterns. The more data that is collected, the more information will be available for better context.
Correlation involves presenting all accumulated data to the relevant departments and analyzing how the data is relevant to the entire business. This includes the collaboration of DevOps, SRE, and IT management teams to diagnose the existing issues and prescribe solutions for any future bottlenecks.
With the collected telemetry and the efforts of the various business teams, a plan is plotted to determine how to act during the remediation process. By automating the majority of remediation and troubleshooting processes, this not only reduces the workload on the IT operations staff but also reduces the possibility of human error and results in faster, more precise solutions overall.
Advanced AI and ML-based algorithms help support a wide range of IT operation processes in a faster, clearer, and less complicated approach. OpManager Plus' AIOps related functionalities include:
Data gathering and processing is one of the fundamental features of AIOps, so irrelevant data is filtered out and removed before it's used. OpManager Plus greatly reduces the amount of noise from the data, allowing ITOps teams to manage alerts and detect incidents before they occur. This reduces the ITOps team's effort as a whole and increases efficiency, while simultaneously boosting reliability.
Automated threshold configuration for critical performance metrics enables network admins to monitor the performance statistics of various devices closely, even in a constantly changing distributed environment. Adaptive thresholds enable automatic threshold configuration for specific monitors based on how the environment behaves at any given time. Learn more
Troubleshooting an issue quickly plays a significant role in reducing the mean time to repair (MTTR) and increasing the efficiency of a network, thereby reducing network downtime. Thanks to an out-of-the-box integration with Slack, OpManager Plus supports one-click integrations with popular communication and ITSM tools. In addition, alerts and notifications can be channeled to communication tools such as Microsoft Teams, Telegram, and more. Learn more.
AIOps aggregates the collected data and identifies relationships and causality, providing IT with an overview of what’s at stake. This enables IT operations teams to correlate and interpret information, as needed, to understand and handle issues quickly. Other related metrics and KPIs such as MTTR, MTTA and MTTF can be obtained, helping to improve event management efforts and the analysis of metrics such as false-positive rates, signal-to-noise ratios, and enhancement statistics. OpManager Plus facilitates monitoring applications from a network perspective, which in turn provides a correlation between application and network performance data. This gives in-depth visibility into the impact caused by the applications on your network and vice versa, and helps you pin-point any faults in your network or applications.
Top suspect causes, the root cause of issues, and the path taken by each individual request can all be analyzed and tracked with with help of a single application. Features such as root-cause analysis and network path analysis help you drill down to the root cause of an issue, collect relevant data, and help remediate it before the end user or client is affected. Once alerted, the IT team is presented with the top suspected causes and evidence leading to AIOps' conclusions. This results in reduced hours of manpower required for routine troubleshooting.
AIOps shines brightest in areas where human struggle, the analysis of vast amounts of data. This is useful for modern, highly distributed architectures where tens of thousands of instances are running simultaneously. An inventory of reports (both manual and automated) allows you to log, analyze, and summarize pragmatic data that is relevant to your network's health and performance. With OpManager Plus, get automated reports sent to your email address or schedule reports for any specific need related to your environment.
AIOps helps automate closed-loop remediation for known issues. AIOps help spin up additional instances of an application to combat slowdowns and quickly remediate any shortcomings. Workflow automation serves network admins, allowing them to automatically run predefined sets of actions in an agile and flexible drag-n-drop manner. Your entire network infrastructure can be visually represented on a single screen. OpManager Plus provides color-coded dashboards and widgets for device status and health of all inter-related IT assets. Learn more about workflow automation
Use ML powered report forecasting feature in OpManager Plus to calculate the number of days left till system resources like memory, disk space and CPU utilization is exhausted. For instance, take device memory usage. By plotting a forecast report, you can see how many days are left till the memory utilization reaches 80 percent, 90 percent, and when it is fully exhausted. OpManager Plus needs a minimum of 3 days training data to apply its predictive algorithms. The forecast is based on usage trends and can change when more data is provided. OpManager Plus can ingest up to 14 days data to accurately forecast usage patterns for system resources.
OpManager Plus also uses ML to generate trend lines for performance metrics. Historical data from within 14 days and current usage trends are ingested by OpManager Plus to accurately predict future usage trends. These trends are plotted on a graph along with historical data so that you can foresee how the performance metrics are going to change throughout the course of the day or week or month. Forecast trends are available for all performance metrics and interfaces. You can also visualize this data as reports and dashboard widgets.
