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Using Machine Learning to Monitor Your Infrastructure

Machine Learning can make it easier to monitor all IT resources across your environment instead of using unique tools for each item in the stack. ML can help you develop an early warning system and even automate failure prevention.

Using Machine Learning to Monitor Your Infrastructure

Content Summary

How Machine Learning Has Automated and Advanced Monitoring
Anomaly Detection and Root Cause Analysis
What IT Organizations Can Do to Set Up for the Future
Conclusion

How Machine Learning Has Automated and Advanced Monitoring

Most monitoring products target one component of the IT stack—servers, databases, logs, cloud resources, and so on. These products were not constructed to monitor the entire network, combined contextually. As a result, changes to the environment (adding a cloud service, for instance, or a new application) frequently require an additional investment in a new monitoring tool. New tools, in turn, mean that IT staff must learn how to use them, or skilled people must be hired to use these products.

Surfacing Signals to Drive Rapid Action

An effective monitoring system will detect issues based on the signs and symptoms as they occur— patterns and anomalies in performance or log data, for instance—and warn users before major problems arise. Automatic anomaly detection based on ML or powerful algorithms can help detect issues and the root causes before extensive business impact occurs. This can be the first step to moving from a proactive to a predictive monitoring approach.

ML algorithms can support anomaly detection for a variety of data sets, including IT metric and log data, root cause analysis, automatic correlation, and dynamic thresholds. IT organizations can use ML to quickly examine vast volumes of monitored data, such as log events, surface the most important information, and then add context to take appropriate action.

In addition, ML can support advanced log analysis, correlating log data, for instance, with metrics and alerts that go beyond simple notifications and provide adequate information to determine why issues are occurring. An early warning system like this can help prevent incidents that can adversely impact a business by processing the signal through a rule-based engine tied into a robust automation framework. As a result, your IT team will be better informed and proactive and experience shorter mean times to recovery.

Preventing Down Time

Companies with frequent outages and brownouts can pay upwards of 16 times the cost compared with organizations that experience less downtime. These businesses also need twice as many staff to troubleshoot problems and spend twice as much time resolving those issues.

The impact is not just on IT but the company and its brand. Consider associated lost revenue, poor customer experience, and harm to brand reputation. Downtime reduces the time and energy available to devote to strategic initiatives and drive business growth. IT teams that have to spend most of their time diagnosing root causes and resolving problems cannot be proactive and focus on major improvements.

An early warning system based on ML can help solve the problem of traditional monitoring, which focuses on static alerting and analysis configurations. Systems like this can help IT teams efficiently run dynamic, distributed environments that help support business goals and maintain a positive customer experience—without missing a beat because of downtime.

Minimizing Alert Fatigue

Broadcom reports that 47% of organizations polled receive a staggering 50,000+ alerts each month. Unfortunately, this tsunami of notifications can overwhelm IT professionals, leading to ignored alerts, slow responses, and incident management failures. This is known as “alert fatigue.”

A warning system that uses ML can ease alert fatigue by classifying alerts and workloads more effectively and surfacing the most relevant alerts from many data types. The system can detect the normal performance range for technical and business metrics and generate alerts based on anomalies by using dynamic thresholds. It can even establish alerts on historical performance and advanced algorithms, which helps IT organizations avoid exhaustion and surface anomalies sooner.

ML-based systems can make the deluge of data that your environment produces more manageable and actionable. And the IT team will have the capacity to support business goals and control costs.

Anomaly Detection and Root Cause Analysis

The root cause analysis (RCA) feature in a warning system is designed to identify the cause of an issue, giving ITOps staff the ability to focus on solving the issue quickly rather than spending precious time looking for the problem. When organizations mix RCA with an ML or AI platform’s ability to monitor just about anything (containers, cloud environments, network, and so on), IT teams can reduce downtime even for highly complex hybrid infrastructures.

Identifying the root cause is the first step in taking a more proactive approach to keeping IT infrastructure healthy. A failure prevention system that uses ML also allows IT organizations to automate actions that remediate the root cause issue, essentially identifying and predicting anomalies and then automatically fixing and preventing them. This equates to reduced downtime plus more time that ITOps teams can spend on innovating and transforming the business.

Dynamic Thresholds Set the Stage for Automatic Remediation

Dynamic thresholds are built on ML-based algorithms focusing on anomaly detection based on the rate of change and seasonality, along with algorithms to contextualize issues. These algorithms automatically detect the normal performance range for any metric—whether it’s a technical or business metric— and accurately send notifications based on values outside of this range that are considered anomalies.

Because dynamic thresholds and the resulting alerts are algorithmically determined based on the data point’s history, they are well suited for data points where static thresholds are hard to identify, such as monitoring the number of connections, latency, and other criteria. Dynamic thresholds are also useful in situations where acceptable data point values aren’t necessarily uniform across an environment.

Dynamic Thresholds are a Requirement for Proactive IT

While static thresholds are too complex and time-consuming for people to manage manually, dynamic thresholds function well in fast-changing environments. Unlike static thresholds, dynamic thresholds are calculated by anomaly detection ML algorithms and are continuously trained by a data point’s recent historical values. By using dynamic thresholds, IT teams can calculate the thresholds to be set and continually adapt to environmental changes generating alerts only when unusual performance is detected.

What IT Organizations Can Do to Set Up for the Future

ML provides strong advantages in rapid troubleshooting, but its capabilities go far beyond tracking and monitoring. It is also an effective way to predict future trends for a business’s monitored infrastructure, using past performance as the basis.

ML can enable IT to address its increasingly strategic responsibilities and challenges, helping teams to discover and resolve issues more quickly and make proactive solutions. After the fundamental elements of an early warning system (anomaly detection, dynamic thresholds, root cause analysis, and forecasting) are set up, IT teams can deliver superior service quality and availability, collaborate and innovate better, and keep their technology aligned to the business outcomes they want most.

Driving Innovation with DevOps

Like ITOps teams, DevOps teams have to manage fast growth and complicated infrastructures. Traditional static thresholds cannot offer the context and agility needed to manage these environments, so modern DevOps teams rely on advanced ML algorithms.

ML algorithms can predict issues before they occur, averting severe problems that can impact business. To minimize excess noise, it also suppresses notifications on problems that don’t require action. DevOps engineers can use the ML algorithms to troubleshoot issues as they occur, helping them to determine if a perceived issue is normal and understand if it was caused by or connected to a change in the environment.

Striving for Continuous Optimization

ML can help IT professionals gain insight and control that would not be possible using traditional monitoring and management approaches. However, it can help IT teams do more than simply respond to new problems.

ML can help ITOps to:

  • Define actions, such as the execution of a script.
  • Set a predefined action in response to an alert.
  • Automate those predefined actions in response to specific notifications using a rules-based engine.

Conclusion

As ML capabilities are more broadly used, organizations can collect additional data and apply these learnings to further enhance and automate monitoring and resolution. As a result, IT organizations can analyze a vast array of big data, examine patterns, make predictions, and drive business.

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