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We depend on large scale systems. Google, Facebook, and Amazon are just a few examples of data centers made up of thousands of machines running complex software applications. Making sure they run smoothly requires high availability, responsiveness, and close monitoring—a task that's become even more critical in recent years.
With the processing for these systems spread across hundreds of subsystems and millions of users, performance anomalies—situations that cause a system to deviate from it's Server Level Agreement—are a serious threat that can result in millions of dollars in losses. This is why, as systems grow and become more complex, professionals need to keep up with the demands of optimal performance and look to new techniques, resources or professional help, such as outsourced performance engineering experts, to help them stay on top of anomalies.
Performance experts are making significant strides in finding ways to not only predict and remedy issues when they arise but also develop strategies to avoid them altogether. The good news: machine learning algorithms are quickly gaining traction for their many advantages in detecting these kinds of issues.
Data is nearing a point where analyzing it manually is becoming impossible. In fact, IDC foresees a Global Datasphere of 175 zettabytes by 2025—a considerable jump from the current 33 zettabytes. Luckily, machine learning algorithms can recognize data patterns, build statistical models, and make predictions by themselves, which would prove invaluable to performance monitoring and management.
Machine learning-based anomaly detection systems are able to help solve performance requirements faster and more accurately than performance teams. Likewise, they offer a helpful resource to tackle the constraints and challenges of static thresholds, as they can incorporate new data and adjust to the changing system accordingly.
They can be used to:
The potential of AI and cognitive technologies has not gone unnoticed in recent years, and machine learning adoption is well on its way with 63% of tech companies already leveraging it. But for companies with performance needs, it's especially important to consider, as some of the most notable advantages include risk mitigation.
Three main risks companies face are:
There are several ways to utilize machine learning algorithms to detect anomalies. Some anomaly detection systems may implement algorithms that identify anomalies based on how far they fall in comparison to a "normal" set of data, whereas others may use algorithms that detect anomalies when the data is too different from other groups or clusters of data.
---Looking for a performance team to help your company keep its systems on track? Schedule a call with a PSL representative to find out how PSL can help you implement performance engineering best practices.