By Jason Bloomberg
The following post first appeared on the Intellyx Brain Blog.
The world of IT operations has changed dramatically in the last decade. In simpler times, say, way back in 2005 before the cloud, tracking down issues in production environments was relatively straightforward.
If some problem cropped up, the first question an ops troubleshooter would ask would be ‘what changed?’. Was there a recent deployment? Did some server just run out of memory? Did the janitor unplug the router again? The conventional wisdom was that one effect had one cause, so find and fix the cause, and voila! problem solved.
For all of you ops veterans reading this, I’ll admit I’m oversimplifying things – but regardless, there’s no arguing with the fact that keeping today’s production environments running at top performance is nothing like how people did it a decade ago.
The difference, of course, is that today, there are too many variables – too many possible causes across multiple environments with complex interdependencies. And on top of all that, everything is always changing.
Welcome to the world of hybrid IT.
Is AIOps the Hybrid IT Panacea?
Hybrid IT refers to some combination of one or more public clouds, private clouds, on-premises virtualized environments, and on-premises legacy environments – keeping in mind that every organization is different and thus has a different combination of one or more of these environments.
But hybrid IT is more than just a multiplicity of environments. In reality, it’s a workload-centric management approach that abstracts the underlying environments, giving organizations the ability to make deployment decisions based upon the particular needs of the workload – especially when those workloads serve customers.
Earlier generation IT operations management (ITOM) tools clearly can’t keep up with this new hybrid IT reality. Enter AIOps – a new category of ops tooling that applies artificial intelligence to the massive streams of data that the systems, applications, and networks generate, in order to better identify root causes of issues and ideally, predict them before they occur.
Read the entire article at https://blog.opsramp.com/aiops-hyperbole
