How Log Analytics are Being Revolutionized by Machine Learning

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One of the biggest advantages offered by machine learning is the ability to not only recognize, but also analyze patterns. It is successfully taking repetitive tasks in many industries and offloading them to be handled by autonomous software.

The field of log analytics has seen serious advancements in the past few years thanks to the evolution of machine learning. The automatic pattern recognition ability saves developers a lot of time, letting them focus their energy on work that really requires input from the human mind.

Understanding more about the innovation that has occurred can help you better see what machine learning can offer. If you want to see the innovation in action, you can get started with Monitor IIS Performance here. Otherwise, keep reading to learn how the innovation of machine learning is altering the current technology landscape.

Make Sense of the Mess with Log Analytics

Today, the logs are generated from an array of servers, apps, and devices, causing countless syntax permutations, along with an influx of information and data that humans are unable to organize manually. By automatically aggregating these logs, you can simplify the process of both log management and log analysis.

Machine learning helps to organize the mass of log data into correlated and cohesive categories. The logs can then be grouped based on time periods, system trends, log origin, user actions and other shared characteristics. Logs that are newly generated are then deposited into the existing groups they correspond to automatically.

The log monitoring and automation helps to reduce the amount of time spent manually categorizing all the logs. While this is true, human guidance is still required to tweak and tailor the aggregation categories to the various concerns and needs of each company.

Say Goodbye to Static Thresholds

Before, setting a static threshold was an effective method for identifying potential performance flaws in a system. For example, it’s possible to create a threshold for monitoring the abuse of a CPU and letting the IT department know if it surpassed a certain, red-line level.

However, this was effective when technical environments were less complicated, with shorter event chains present between the system components and fewer unique errors were generated. Generating static thresholds may allow you to catch flare-ups in certain areas, but thresholds can’t be set for occurrences that are completely unanticipated.

Even the word “static” is outdated compared to the agile world present today. From month to month, system environments are evolving rapidly. Not all the participants in a system’s architecture are always aware of the changes made by other departments.

An example of this is a developer who is not always aware of regular server-side changes, and how thresholds must be adjusted to accommodate these changes. In extremely dynamic environments, a static threshold must be repeated and adjusted manually, which wastes brainpower and time.

The Potential for a Huge Impact

Machine learning, pertaining to oncology and log monitoring analysis, can’t cure cancer. However, it can accurately identify cancerous cells, which makes the influence formidable. On micro levels, machine learning has empowered log analysis, allowing every team to eliminate the manual, repeatable and routine tasks.

When this happens, the most valuable resource an organization has – the employees – can begin working on what machines can’t handle, which is providing deep thinking abilities to problem solving and conceiving of or creating new and innovative products.

On a macro level, this type of advanced log analysis may help to prevent serious calamities that could affect the entire world. For example, the Heartbleed bug made it past the overworked and tired eyes of the knowledgeable and extremely capable eyes of the developers, remaining unreported in the landscape of online security for several years. Machine learning could have successfully identified the vulnerability earlier preventing the loss of private data.

The evolution of log analytics thanks to machine learning is ongoing and something that’s expected to continue evolving for many years to come.

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