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Using Machine Learning to Detect Anomalies in Performance Engineering

Using Machine Learning To Detect Anomalies In Performance Engineering

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.

Why Machine Learning?

 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:

  • Help determine statistical models of “normal” behavior
  • Use these models to predict future values
  • Compare predicted values to actual ones, as data is collected in real-time.

A great advantage of machine learning algorithms is that they learn over time. When new data is received, the model can adapt automatically and help define what “normal” is month-to-month or week-to-week. This means we can account for new data patterns and make more accurate predictions and forecasts than ones based on the data’s original pattern. Best of all, these updates would happen without human intervention.

What Would This Mean for Enterprises?

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:

  • Financial loss
    Probably the most publicized of the effects of faulty performance, it’s often highlighted because losses fall in the millions of dollars category. On average, network downtime can cost a company $5,600 dollars per minute, so every second will count.
  • Loss of clients
    Clients today have higher expectations than ever before. With faster, more efficient platforms, few users are willing to tolerate faulty or slow systems. Failure to address performance issues will result in client desertion due to bad customer experiences.
  • Loss of reputation
    Possibly more harmful than the previous two, loss of reputation presents a significant barrier to any incoming business. Unhappy clients will not hesitate to relay their poor experience and discourage others from using your services.

However, ensuring a system is exceeds user expectations goes a long way in turning these customers into your #1 supporters. This task can be made easier and more efficient by applying machine learning algorithms to stay on top of any issues that might arise and expand your business without overwhelming your Ops team.

Getting Started with Machine Learning Algorithms for Performance Monitoring

?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.

No matter which is chosen, it’s essential to first establish your system’s specific needs. Taking these into accounts, a team can determine which machine learning model best serves the specific requirements and what metrics the model needs to report on in order to gain valuable insight into the system’s performance.

Perhaps most important of all, a definition of what falls under “normal” behavior and what falls under “anomaly” must be established for the algorithm to function properly. This allows the model to understand what it needs to look for and fine tune the concept as it goes.

Nonetheless, human insight should always be the guiding spirit behind any and all optimization endeavors. Making the calls on changes, tweaks or any other decision needs to lie in the hands of experienced performance engineers that can determine the best strategies to keep systems working.

Finding experts in this growing field can be challenging, it’s possible to address your company’s performance needs by teaming up with a nearshore software outsourcing partner. Nearshoring makes a vast pool of global talent accessible. You’ll have access to real-time collaboration through cutting edge communication technology with virtually no delay.


Looking for a performance team to help your company keep its systems on track? Schedule a call with a Perficient Latin America representative to find out how Perficient Latin America can help you implement performance engineering best practices.

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