Enterprise software, as well as other kinds, remains a complicated endeavor, thus necessitating the use of modern means to gauge, analyze, and adapt their performance. And one of the most popular technologies in the performance engineering market right now is machine learning. Since it has demonstrated an unparalleled ability to not only help foresee performance issues and fix them. When used in the right manner — this combination can also help performance engineering teams to steer clear of any issues at all completely. It is because machine learning comes equipped with the ability to interpret and analyze data in real-time, thus delivering valuable insights about the system’s performance.
However, if you are going to truly leverage machine learning’s abilities in the context of performance engineering, it is first essential to understand the basics. Through this article, let’s discuss the kind of performance anomalies one can encounter.
- Point anomalies: This is when there is only one data point that is distinct from the entire set.
- Contextual anomalies: In this scenario, the anomaly is contextual, i.e., exists only in a particular context.
- Collective anomalies: This refers to a data set that exhibits signs of an anomaly.
To lend more context, let’s see how these anomalies would be detected without help from machine learning.
- One way is to wing it based on experience, i.e., experienced folks execute manual checks concerning specific performance markers
- Engineers could also establish thresholds for performance in specific aspects and take corrective measures when they are breached
- Another way is to compare the system to a benchmark, but this can be a challenge since systems are continually being upgraded
It is not the most efficient approach to performance engineering. Thankfully, machine learning can step in here and efficiently tend to the task – how, you ask. Well, there is more than one way. But the basic idea is that it helps performance engineers establish benchmarks for the system’s expected behavior — a baseline if you will. But now, let’s take a look at some of the top strategies for leveraging machine learning for detecting performance anomalies.
- A common strategy underpinned by the K-Nearest Neighbours algorithm, here the assumption is that standard data points will be found in a dense area. It implies that data anomalies are at a distance
- Yet another leading choice of method for performance anomaly detection with help from machine learning is the clustering method. Here, the assumption is that similar data points are part of similar groups. So, the K-means algorithm then generates similar clusters of data points and red flags, the ones that do not conform to the clusters.
- Though Support Vector Machine is a supervised learning method, one can always make use of extensions for performance anomaly detection.
It is a given that software must be able to deliver their best performance at all times. And to ensure that, you can always rope in a trusted name for performance engineering services, which can help you integrate machine learning into your strategy for significant results.