What Is AIOps and Its Use Cases
Everything you need to know about AIOps
Introduction
Artificial Intelligence is transforming almost every space and industry, be it marketing, sales, human resources, and IT operations is not far away. We are generating more data than ever, as a result, more servers are being set up to store and process this data and it is required to continuously monitor the servers to stop any outages (close down of servers) from happening. Still, the enterprises face outages because the current monitoring systems are reactive (an issue occurs and then IT operations teams get notified about it).
Impact of outages!
6000 Global IT leaders were interviewed by Cisco across Australia, Canada, France, Germany, the United Kingdom, and the United States. Here are some noteworthy discoveries :
- The average cost of an enterprise service outage in the US is $402K and in the UK it is $212K.
- 97% of the leaders reported at least one service outage related to the business-critical application.
- The average mean time to resolution (MTTR) of the issue hovers at seven hours.
The above challenges are due to the reactive monitoring systems being used by these enterprises currently and can be resolved with AIOps.
What is AIOps?
The term AIOps stands for “Artificial Intelligence for IT operations”. It is the application of Artificial intelligence/Machine Learning for proactively detecting, and resolving the issues faced within IT operations.
How AIOps can help?
- Go from reactive to proactive issue management.
- Achieve faster mean time to resolution (MTTR) for the issues.
- Drive faster and better decision-making by automating the issue resolution process.
AIOPS use cases
The following are the top use cases of AIOps:
- Proactive Anomaly detection
AIOps enables the anomaly detection of the issues faced by the IT servers by monitoring the large volumes of server data. With AIOps, the enterprise can proactively monitor the metrics of the servers and detect anomalies before it becomes too serious. For example, consider an AIOps system for tracking the disk utilization of the servers. If the disk utilization rises suddenly then the AIOps system will detect this anomaly and alert the IT team before the actual issue occurs.
2. Root Cause Analysis
Multiple server-related aspects such as disk, memory, CPU, I/O etc might go wrong for a server and it becomes challenging for the IT operations team to detect the root cause. The AIOps give visibility on the correlation between incidents(or server metrics) by observing every metric of the server and thus helps the IT team to correctly diagnose the issue. For example, in the trend chart shown below, disk utilization starts rising, and as a result memory and CPU utilization also rise. AIOps system can detect that the root cause of this rise in metrics is the rise in disk utilization.
3. Intelligent Alerting
One of the challenges of traditional reactive issue management systems is that they raise a huge number of alerts. IT teams set some predefined thresholds for the server metrics, for example, the 90% threshold for the disk utilization metric of the servers. Now, whenever the disk utilization of a server crosses 90%, it will raise an alert. Because every server behaves differently, this static 90% cut-off does not work for every server leading to a lot of false alerts.
AIOps solutions, by learning from historical data, raise only the accurate alerts and thus reduce the total number of alerts.
4. Automated Issue resolution
Once the root cause of the issue is identified by the AIOps solution, IT teams can go one step beyond and use machine learning or some ETL jobs to trigger automatic issue resolution processes to remediate the issues.
5. Capacity planning
As the AIOps enables proactive anomaly detection, IT operations teams know what issue might occur in the future and thus they can plan the resources accordingly to focus on the upcoming issues and resolve them in advance.
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