Machine learning analytics is taking its place in IT. 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 However, companies are still managing them with outdated approaches.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?