Today, we’re seeing more and more businesses transitioning their business critical applications that demand consistent high performance to virtual environments. This trend has created a new set of hurdles for IT teams. The shared resources and intrinsic complexity of virtual environments make finding and fixing performance issues much more difficult than in traditional environments. However, companies are still managing them with outdated approaches. They monitor and analyze compute, storage, network and application in separate silos, using tools that measure individual metrics (e.g. CPU utilization) and fire off alerts every time the threshold is exceeded. The result – hours of IT time tuning thresholds, wasted on manually reviewing and evaluating hundreds to thousands of alerts to find and fix application performance issues. Virtual data centers are simply too large and too complex to manage this way.
So, why is IT turning to machine learning analytics?
Advanced machine learning and deep learning analytic tools solve this problem by identifying the root cause of application performance issues in virtual environments instantaneously, and recommending specific steps to resolve them. They look across the IT silos and learn the complex patterns of behavior between interrelated components in the virtual ecosystem. This allows them to identify even complex, subtle interactions, such as “noisy neighbor” scenarios where the slow performance on one VM may be caused by behavior of other VM’s that share resources. Most importantly, advanced machine learning analytics tools can predict when performance issues will arise, based on the past behavior.
This behavior-based approach automatically delivers precision and accuracy – enabling it to prioritize and inform IT about the issues that will result in real problems, and to identify problematic subtle issues that are lost in the noise or missed entirely by threshold-based tools. Predictive analytics enable machine learning based tools to use the learned patterns of behavior and resources needed to forecast impending issues. This allows IT to prevent problems before they occur.
How does this affect IT?
Machine learning tools give IT their jobs back. Instead of spending their days reacting to and reworking application performance issues, IT teams spend their time on work and projects that add true business value, and drive company objectives forward. Machine learning IT analytics tools provide IT with the accurate information they need to make sound decisions and the confidence to make changes without fear of causing unintended performance issues. It is becoming a key enabling technology for supporting core business operations with IT services that are responsive, agile, and flexible.
