Put an End to Trial and Error with Machine Learning Analytics
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.
Read the entire article here, Put an End to Trial and Error with Machine Learning Analytics
via the fine folks at SIOS.