3 Clicks to Root Cause

How Smart View uses dependency models and machine learning to intelligently show you what's relevant

Determining where the root cause of a data center issue might be can be a frustrating process. It might be a storage array disk drive or a set of failed fans causing a processor slowdown, or a port channel running under capacity with a yanked network cable, or any of a thousand other issues. In a large data center, there might be a dozen concurrent issues and thousands of sympathetic failures downstream from the real failures. What if we could look at any piece of our infrastructure and know everything it depended on and everything that depended on it? What if we could instantly spot where there were issues up and down that list of dependencies?

During this webcast, Kent Erickson and Anirban Chatterjee from Zenoss will demonstrate how following this approach can drastically reduce MTTR. They'll show:

  • The necessity of broad and deep infrastructure models in order to generate dependency graphs
  • The use of Zenoss Smart View to explore device dependencies across the timeline
  • The use of crowdsourcing and machine learning to automatically propagate the most relevant devices to the top of the heap
3 Clicks to Root Cause

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