Using Machine Learning for Root Cause Analysis

Using Machine Learning for Root Cause Analysis

From a security breach to a complete system outage, when an incident occurs and your network or service is impacted, it’s typically the result of a chain of events. A problem with one service has impacted another service, and so on until finally, you’re facing a problem that’s compromising availability and damaging your customer experience. 

In the event of a serious incident, your team’s immediate response is to focus on identifying the root cause and restoring service. Because the chain of events for outages typically involves a combination of technical and process issues, it can be hard to identify the root cause and understand why the issue occurred in the first place.

Understanding the Why 

Identifying the root cause can often be complex. In order to understand why an issue has occurred, you need to uncover the underlying cause. 

Oftentimes, what is needed to identify the underlying cause is to understand what changed. Manually searching through your metrics or logs to identify what has changed can consume hours of valuable time, which is why an efficient root cause analysis (RCA) process and having the right analysis tools in place is vital. Not only will an efficient and intelligent RCA process help you identify the problem faster, but it will also help you build corrective action plans for continuous improvement.

If You Can’t Monitor It, You Can’t Analyze It

If your systems are highly distributed, are you able to ingest and monitor data from all of them? Many network monitoring and root cause analysis tools (either by design or by configuration) are restricted in the data sources and data types they are monitoring, making them less than useful tools for efficient problem solving and finding the real cause of an incident.

In fact, the restrictive nature of traditional tools means that, on average, a typical organization analyzes less than 1% of its available data.

Root cause analysis is all about cause and effect. You need to understand what changed in order to understand the impact that it had. That means using a solution that is able to ingest all of your data, regardless of the source. 

The Power of Machine Learning in Root Cause Analysis

With LM Logs log analysis capabilities, we’ll be analyzing the data of every system within your infrastructure to learn its normal behavior and build a database of event structures based on the incoming events it analyzes.

The algorithm can determine the relevancy of each new individual event by comparing its structure to the learnings database. An event is then classified anomalous if it does not match an event in the learnings database. By identifying anomalous events the underlying change and root cause becomes more understandable and easier to find. 

The greater the amount of data received, the easier it is to draw quick and correct conclusions and gain deeper intelligence. For example, consider how a software bug evolves. As your software components become erratic and unpredictable, new data points will explain the origin and evolution of this scenario. But where did it start? In what entity? What entity can we rule out?

Control Your Response

No system is perfect. Issues will happen. You have no control over that. But what you do have control over is how early you respond to and correct events that have the potential to escalate in impact. Our upcoming capabilities with LM Logs will enable us to continue to seamlessly monitor infrastructures more effectively while enabling teams to detect issues earlier, improve root cause analysis efforts, and increase uptime, stability, and security. You will also free up resources and reduce both risk and cost.

To learn more about LM Logs, talk to your LogicMonitor Account Manager or Customer Success Manager, or put your name on the beta access waitlist here.

Goran Sandahl

Director of Engineering, Log Intelligence

Goran Sandahl is an employee at LogicMonitor.

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