As enterprise software platforms expand in complexity and importance, performance anomalies have become a serious threat that can result in millions of dollars in losses. Faced with this challenge, performance engineering experts have begun utilizing machine learning algorithms to predict performance issues, remedy them, and even avoid them altogether.
Machine learning solutions can analyze and interpret thousands of statistics per second, providing real-time (or near real-time) insight into a system's performance. They can be used to recognize data patterns, build statistical models, and make predictions that are invaluable to the process of performance monitoring and testing.
With these abilities, machine learning tools are able to solve performance issues faster and more accurately than performance teams, significantly improving efficiency. Furthermore, they can help teams understand the platform's behavior quickly while mitigating the risks associated with poor performance, such as reputational damage, a reduction in customers, and financial losses.
Here we take a closer look at the necessary considerations for organizations looking to harness the power of machine learning algorithms to improve performance anomaly detection and, as a result, overall performance testing.
During testing, there are numerous signs that an application is producing a performance anomaly, such as delayed response time, increased latency, hanging, freezing, or crashing systems, and decreased throughput.
The root cause of these issues can be traced to any number of sources, including operator errors, hardware/software failures, over- or under-provisioning of resources, or unexpected interactions between system components in different locations.
There are three types of performance anomalies that performance testing experts look out for.
In all cases, anomaly detection is similar to what's known as noise removal or novelty detection during the performance testing process. The difference is that while anomaly detection looks to flag potential threats, novelty detection works to identify patterns that were not observed in training data, and noise removal works to separate unwanted observations from the desired data for analysis.
[RELEVANT READING: Using Machine Learning to Detect Anomalies in Performance Engineering]
One of the most basic methods of anomaly detection in performance testing is to identify and flag data points that stray from the common model through simple statistical techniques.
Here a team may set a threshold for specific performance metrics, like CPU utilization, disk I/O, memory consumption, or network traffic, and raise alarms when that threshold is violated. The challenging aspect of this approach is that larger data systems can have variable workloads, so setting static thresholds can trigger false alarms and won't help the team understand the effect of application changes or updates to performance.
In this category, teams are continuously evaluating a system by comparing it to baselines or statistical models. Since systems are continuously evolving, baselines are actually very rare. Additionally, this charges the team with the arduous task of keeping performance models up to date with the system's changing behavior.
This method relies heavily on the past experience of trained "gurus" that use important performance trackers to perform manual checkups and work mainly based on personal observations and routine inspections.
Machine learning can be used to help determine statistical models of "normal" behavior in a piece of software. They are also invaluable for predicting future values and comparing them against the values being collected in real-time, which means they are constantly redefining what "normal" behavior entails.
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 the ones based on the data's original pattern. Best of all, these updates would happen without human intervention.
There are several ways machine learning can be utilized to detect anomalies in performance. Here are a few of the most popular methods:
Now that we have these methods in mind, let's look at how to establish what a specific system's needs are, and how to satisfy them effectively to fully realize the potential of a machine learning system for performance testing.
[NEARSHORE SOFTWARE OUTSOURCING | Start Leveraging Machine Learning with the Experts. Let's Talk]
It's essential to first establish which machine learning model best serves the platform's specific requirements, and what metrics the model needs to report on in order to gain valuable insight into the system's performance.
It's extremely important to define "normal" behavior in the system, as well as determining what is considered an anomaly. This information allows the machine learning model to understand what it needs to look for and fine-tune the concept as it goes.
Human insight should always form the foundation of these optimization endeavors. Decisions on changes or tweaks to the system must lie in the hands of experienced performance engineers that can determine the best strategies to keep systems working.
Here are some strategic considerations that must be addressed before tackling the difficult task of developing a machine learning platform for performance testing.
Finding experts in this growing field can be challenging, so it's worth forming a partnership with a mature nearshore software development outsourcing provider to address your company's performance needs. Nearshoring makes a vast pool of global talent accessible, providing world-class performance and machine learning experts at a fraction of the domestic cost.
Today's organizations are discovering that peak software performance is not just a benefit for customers but a necessity. An inability to respond quickly to system lag or crashes can result in a great financial and reputational loss that should not be taken lightly.
A company with the right foresight can utilize machine learning technology to take a more preemptive approach to performance anomalies, resulting in a system that exceeds user expectations. This goes a long way toward turning customers into loyal supporters of a brand, while also empowering companies to expand their business without overwhelming their development and operations teams.
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.