When end-users report slow performance in business-critical applications, IT teams everything to fix the problem as quickly as possible. In virtual environments, where the root causes of problems are rarely straightforward, they may spend days trying and testing multiple different solutions. Troubleshooting this way creates a huge drain on IT time and resources – and even occasionally, morale. IT teams want to be innovators who add value to their business operations with new technology that automate manual tasks, increase end user productivity, streamline costs and respond to business needs quickly and flexibly. Unfortunately, without the insights and automation that machine learning analytics provides, IT departments are wasting more and more time and resources on low-value problem-solving.
Virtual Infrastructures are Too Complex
for One-Dimensional Approaches
What is causing this problem-solving quagmire? IT is running more business critical applications in complex, dynamic virtual infrastructures where traditional diagnostic and monitoring tools cannot identify root causes of application performance issues or provide specific steps to solve them. IT teams are still looking at their virtual infrastructures in individual operational silos – compute, application, storage, and network. They are using multiple tools to gather information about each silo and then piecing the results together manually to devise a theory about the root cause and a strategy for resolution.
Threshold-based Tools and Old-School Approaches
In a recent survey SIOS conducted, 78 percent of respondents are using multiple tools to identify the cause of application performance issues in VMware. Only 20 percent of respondents said the strategies they are using to resolve these issues is completely accurate the first time.
Legacy monitoring tools use threshold-based technology that was originally developed for physical server environments. They help you keep physical components operating within specific parameters, such as CPU utilization, storage latency, and network latency. You manually set the parameter thresholds for every metric you want to monitor in every silo and these tools will alert you every time a threshold is exceeded – often hundreds of times for a single incident.
More Data is Not More Information
In virtual environments, virtual resources share the physical host, storage, and network resources. These components work together in complex interrelationships that often mask the root causes of performance issues. IT pros responsible for each silo have to decipher hundreds of alerts and pinpoint what matters using their subjective opinions and good old trial and error.
Fortunately, new machine learning analytics solutions like SIOS iQ use deep learning techniques to look across the silos, factor in the interrelationships of virtual resources, and identify the root causes of application performance issues. They use predictive analytics technology to identify the issues that will cause performance issues in the future so you can avoid them. They provide a degree of automation, precision, and accuracy that humans with threshold-based tools cannot approximate.
Machine Learning Analytics Eliminates Trial and Error
Machine learning analytics tools tell you how to resolve the issues. You don’t need to weed through hundreds of alerts or compare dashboards filled with charts to diagnose the problem. You get the info you need without the expertise of a data scientist. With machine learning analytics, there is no need for data selection, modeling, preparation, extraction or configuration is necessary. SIOS iQ tells IT which infrastructure anomalies are important and which are minor so they can prioritize their valuable time.
With new and advanced machine learning and deep learning tools, IT teams can move from a reactive to proactive state. That means you can spend more time innovating and less time on trial-and-error.