By navigating our site, you agree to allow us to use cookies, in accordance with our Privacy Policy.

AlOps to demonstrate multi-domain data virtualization framework

Hybrid multi-cloud deployments present IT operations teams with unique challenges because application delivery spans public networks and cloud infrastructure that the enterprise doesn’t own, operate or control.

At ONUG Fall 2019 in New York City (on Thursday, October 17 at 9:40am), the AIOps for Hybrid Multi-Cloud Working Group will demonstrate a multi-domain data virtualization framework that was initially presented at ONUG Spring in Dallas this past May.


ONUG LOGO-2The first step in applying AI to automate performance monitoring is to aggregate, normalize and enrich select state data that is sourced from many points across multiple independent domains.


“Just like a great wine starts with great grapes, great AIOps starts with great data,” says Nick Lippis, ONUG Co-Chair and Co-Founder. “The ONUG AIOps Working Group is addressing the fundamental challenge of gathering disparate state data spread across multiple domains and providing unified access to normalized data by machine learning and AI tools.”


“The growing adoption of Agile & DevOps is putting pressure on IT operations teams to quickly sort through the critical metrics, logs and traces generated by multiple monitoring systems that are tracking the performance of different applications, services, systems and networks on premises and in the cloud,” said Tsvi Gal, ONUG Board Member and Managing Director of Enterprise Infrastructure for Morgan Stanley.


“Collecting all the raw telemetry data and storing it in a massive data lake without filtering and analytics is costly, slow and inefficient. ONUG’s AIOps initiative is a promising approach to selectively preparing and providing access and synthesis of the right datasets for automated AI-driven analysis and operations tools.”


“The enterprise IT industry’s ultimate goal is to ensure cloud-based application availability, performance and security across the entire expanse of hybrid multi-cloud infrastructure through a single interface,” said Chris Drumgoole, ONUG Board Member and CIO of GE. “But it is challenging to administer a central data lake when IT operations have a global footprint. The ideal solution will provide access to the right set of state data based on each application’s dependency map and use machine learning and AI techniques to automatically perform the necessary correlations to determine what is happening.”


“Since the inception of Mist, we have been on the journey to develop an AI solution — ‘Marvis’ — that can answer business-critical questions on par with network domain experts,” said Bob Friday, CTO of Mist, a Juniper Company. “Through the efforts of this ONUG Working Group, Marvis and other AIOps solutions will be able to answer and solve IT problems with high granularity and confidence.”


“The ONUG AIOps initiative addresses one of the most vexing challenges in IT operations: how to diagnose root cause when data is spread across separate data silos in different locations  and inside a myriad of vendor products. Along with Juniper Networks and VMware, we’ve proven that vendors can adopt a common standard that breaks down these walls for the benefit of our shared customers,” said David Mariani, Co-Founder and Chief Strategy Officer of AtScale.


For more info, click here


Nitisha Dubey

I am a Journalist with a post graduate degree in Journalism & Mass Communication. I love reading non-fiction books, exploring different destinations and varieties of cuisines. Biographies and historical movies are few favourites.

Related Articles

Upcoming Events