Prologue for EMA Research Project: Machine Learning and Artificial Intelligence in Enterprise IT and DevOps

Sep 18, 2017 2:29:07 PM

Why are there still so many repetitive tasks in data center and cloud management today? Why does application management still contain so many manual steps? Why do most organization still suffer from automation and monitoring silos that prevent them from avoiding preventable application outages and service degradations?

When I talk to the data center guys, they often tell me: “we are already optimally automated. We run DRS, storage vMotion, auto scaling, automated backups, and we do log analytics for root cause analysis.” But if this was really the pinnacle of automation it would mean that we would never get past spending 50% of our IT budget on ‘keeping the lights on.’ It would also mean that enterprise IT could never be truly aligned with strategic business goals, as none of the above ‘intelligent technologies’ provide this capability.

What Machine Learning and Artificial Intelligence Need to Achieve

Machine learning and artificial intelligence (ML/AI) in IT Operations needs to look over the shoulders of all the different roles of data center staff and learn how they handle their daily, weekly, monthly, quarterly and annual maintenance tasks. ML/AI needs to also observe how new resource and application requests are handled, based on corporate policy and compliance requirements. ML/AI should also learn across organizations to tell IT not to combine VMware patch xyz with BMC patch abd, as otherwise application fgh will crash. In addition, ML/AI should keep an eye on all corporate IT and business metrics to predict future problems before they arise.

Breaking Down Traditional Thought Barriers

Storage is storage, the NOC is the NOC, databases are databases, VMs are VMs and apps are apps. They all come with thousands of granular configuration and management tasks. A modern IT strategy however should be based on an application and service centric IT operations paradigm with the ultimate goal of getting close to 100% of resources spent on innovation and close to 0% on ‘keeping the lights on.’

Here are two interesting vendors who have already started to leverage machine learning for transforming IT into an accelerator of business innovation.


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Torsten Volk

Written by Torsten Volk

With over 15 years of enterprise IT experience, including a two-and-a-half-year stint leading ASG Technologies' cloud business unit, Torsten returns to EMA to help end users and vendors leverage the opportunities presented by today's hybrid cloud and software-defined infrastructure environments in combination with advanced machine learning. Torsten specializes in topics that lead the way from hybrid cloud and the software-defined data center (SDDC) toward a business-defined concept of enterprise IT. Torsten spearheads research projects on hybrid cloud and machine learning combined with an application- and service-centric approach to hyperconverged infrastructure, capacity planning, intelligent workload placement, public cloud, open source frameworks, containers and hyperscale computing.

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