Turbonomic Brings Autonomic Computing Back to IBM — But This Time It Might Just Work

IBM, the pioneers of autonomic computing, the concept of creating self-aware systems that automatically optimize their own cost, availability, and user experience (Robert Morris, IBM, Almaden Research Laboratory, 2001) announced their intend to acquire Turbonomic, one of today’s most exciting deep-learning and streaming-analytics driven application resource management plays.

“The growing complexity of the I.T. infrastructure threatens to undermine the very benefits information technology aims to provide” IBM manifesto on autonomic computing from 2001.

What Turbonomic Brings to the Table

The chart shows most of the 6 categories, 27 subcategories, and 911 products for organizations to create their cloud native application stacks from

Turbonomic has created inference models that continuously watch and evaluate resource allocations and configurations within their individual business context. For example, the platform may see 100 Java apps running on top of a VMware hypervisor stack, all of them doing just fine. However, information coming from the vCenter API shows Turbonomic that 7 out of these 100 apps have been accumulating an increasing level of storage latency. API data from the DataDog APM shows no sign of user experience degradation and the ServiceNow API shows no tickets associated with any of these 7 apps. Turbonomic might now wait until Monday morning, as of course all of this happened over the weekend but is clearly not an emergency, to make an offer the VMware admin team will most likely not refuse:

“The following 7 apps show an increasing number of storage latency. This is due to increased use and the fact that all 7 of them share the same storage volume. Would you like me to tell vCenter to separate storage of these apps so that they share storage volumes with apps that are much less busy? Press OK to proceed.”

Eric Wright (Technology Evangelist at Turbonomic) and Rick Ochs (Principal Product Manager at Turbonomic) discuss and show how Turbonomic works.

In reality, there would be hundreds or thousands of additional variables playing into this decision, some of which are based on hard and fast rules (regulatory compliance might not allow moving some or all of the data) while others will be based on lessons learned from approaching similar situations in the past in a similar manner. Maybe one of the 7 apps relies on other storage volumes that constantly exchange data with our culprit volume and therefore moving this volume might also require moving other volumes, which in turn could negatively affect different apps. While this type of inferencing might not be a challenge for deep learning models, human operators would most likely not be able to identify and cross-correlate the data necessary to arrive at the same conclusion.

Bringing Down Public Cloud Cost

Turbonomic + Instana + Watson = Autonomic Computing 2.0

Artificial Intelligence, Cognitive Computing, Automatic Machine Learning in DevOps, IT, and Business are at the center of my industry analyst practice at EMA.