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Incident management is one of the most crucial DevOps and IT support processes. So, it’s essential that teams get it right. The costs of downtime and service outages can add up quickly. Therefore, you need an efficient team and process to shorten the incident lifecycle and reduce outages. The opportunities presented by machine learning in the incident management lifecycle are becoming too good to ignore.
When an incident occurs, it goes through a fairly structured incident management workflow; Detection > response > remediation > analysis > readiness. Every step of the incident lifecycle requires interaction of some kind – typically a combination of automation and human involvement. Machine learning will play an integral part in the future of shortening the incident lifecycle and reducing MTTA/MTTR.
As your organization evolves and software development speeds up, you’ll implicitly bring more technology, scale, and variation to your applications and infrastructure. Unfortunately, the traditional way of detecting and managing incidents can’t keep up with the vast amount of events generated by complex, highly integrated architecture.
IT operations can no longer analyze, identify, correlate and troubleshoot all incidents manually on time. With the latest updates in machine learning and artificial intelligence, you can leverage the power of AIOps and MLOps. AIOps and MLOps allow your IT and DevOps teams to implement and automate customized incident management procedures, which is why organizations like Yahoo, Cisco, and HCL are using machine learning for incident management.
Machine learning is involved in our day-to-day lives – things like online search requests, auto-filtering spam out of email inboxes and speech commands on smartphones are only a few examples. Incidents like server downtime, disk-usage beyond the threshold and degraded performance of assets will disrupt organizational operations and hinder productivity. Not surprisingly, organizations are now using machine learning in incident management to:
Let’s look at some use cases for how you can use machine learning for incident management:
Imagine one of your operators is working with the support team on an incident about losing application-server connectivity. On the other hand, another operator is discussing a different incident (firewall-related) with your network provider. After spending an hour on the incident response for two separate alerts, they realize they’re working on the same incident. If they had shared insights, they could have correlated the incidents.
Many alerts are directly and tangibly related to one another – creating one singular incident. If these connections are not identified quickly, the incidents are treated as discrete anomalies, resulting in multiple tickets and workflows. The most common assumption in DevOps or IT operations is that incidents occurring at the same time are correlated. However, two events happening at roughly the same time doesn’t necessarily mean they’re related.
Machine learning can apply multiple levels of correlation factors to your data, ensuring related incidents are highlighted and managed accordingly.
It’s common in a production environment for incidents to repeat themselves. Machine learning can identify patterns and the behavior of associated events responsible for each incident or anomaly. It can then use these fingerprints to anticipate similar patterns and event behavior that might occur in the future, giving a heads-up to your DevOps or IT team.
Let’s take a common scenario of your disk space usage reaching 98%. You might face this situation every single day. Though alerts are set to notify the user about the situation, what if disk usage rapidly spikes and skips the warning period? Then, the database will go down, along with all the connected apps.
Machine learning can actually save the day here. It detects the similarities from previous alerts and system behavior related to your storage, database and applications, and could notify you if a similar incident is likely to occur. Machine learning for incident management can help you find patterns between incidents, identify the root cause of a problem and tweak monitoring and alerting tools to improve incident detection (disk space alert on the database server in this scenario).
Thus, you can use machine learning algorithms to automatically look for early warnings of incidents that might occur in the future. This dramatically reduces incident MTTA and MTTR for DevOps and IT operations since they’re now aware of upcoming incidents and can likely prevent them before it’s too late. With machine learning, help desks can automatically trigger notifications or create tickets for anticipated incidents so your team can immediately start working toward a solution.
For example, as soon as your application server’s performance starts deteriorating, your team can anticipate potential failure from the past performance data of the server, notify the on-call user, create a ticket and associate all related tickets with the incident.
A good number of incidents occur due to performance degradation of older IT assets. Machine learning can automatically identify assets that tend to break down repeatedly. Once the assets are detected, your help desk can use machine learning algorithms to send notifications and fix the issue. As a simple example, the help desk could automatically raise a request to replace the printer’s toner after the printer has printed x number of pages.
By automating incident management processes, organizations can achieve faster service restoration, saving time and money. Accurate and up-to-date information is vital for incident management and operational efficiency. Machine learning algorithms can pull data from numerous systems and provide critical information, along with dependencies between the components to your DevOps and IT operations teams.
With machine learning at your fingertips, the incident lifecycle can be reduced from hours to seconds. The analysis and real-time insights provided by machine learning algorithms can help you rapidly respond to incidents, identify patterns for any incident, correlate similar events and anticipate incidents that might occur in the future.
Intelligent implementation of machine learning and automation during incident management can facilitate process, team and tooling improvements. And, as machine learning for incident management drives efficiency, it also improves stakeholder communication – minimizing the overall business impact of major incidents.
When an incident strikes, DevOps and IT teams need automation and collaboration tools right at their fingertips. Luckily, VictorOps is both. Try a 14-day free trial of VictorOps to combine the power of your monitoring tools with on-call scheduling, alert automation and collaboration in one place – making incident management suck less.