Dan Ortega - Vice President of Marketing
During the next 5 years, machine learning is poised to play a pivotal and transformational role in how IT Infrastructure is managed. Two key scenarios are possible: transforming infrastructure from a set of under-utilized capital assets to a highly efficient set of operational resources through dynamic provisioning based on consumption; and the identification of configurations, dependencies and the cause/effect of usage patterns through correlation analysis.
In the world of IT infrastructure, it’s all about efficient use of resources. With on-premise infrastructure (compute, storage and network) utilization rates for most organizations in the low single digits, the cloud has sold the promise of a breakthrough. For those organizations moving to Infrastructure as a Service (IaaS), utilization in the middle to high teens is possible, and for those moving to Platform as a Service (PaaS), utilization in the mid-twenties is within reach.
Dynamic provisioning driven by demand is essentially the same operational concept as power grids and municipal water systems – capacity allocation driven by where resources are consumed, rather than where they are produced.
The second part of the breakthrough relates to right-sizing infrastructure. Whether this is network capacity or compute Virtual Machine size – machine learning will enable analysis of the patterns of behavior by users and correlate them to the consumption of infrastructure resources.
During the near term, these benefits will be much more tactical. Automated discovery combined with behavioral correlation analysis will virtually eliminate the need for manual inventory and mapping of components and configuration items in the IT ecosystem to reveal how the ecosystem is operating.
Today, IT has the opportunity to automate the mapping of components in their infrastructure to provide a more accurate and actionable picture.