Framework Provides an Abstraction Layer for Accessing State Data Sourced from Multiple Domains and Consolidated in Cloud Repositories for Analytics Powered by Machine Learning and AI
ONUG, the voice of the Global 2000, is focused on the application of Artificial Intelligence (AI) to overcome the operational challenges of ensuring application performance in complex hybrid multi-cloud environments that span multiple independent operational domains. At ONUG Fall 2019 in New York City (on Thursday, October 17 at 9:40 am), 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.
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. The 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. This data, which is maintained in a set of cloud-based repositories, is accessed via a virtualization layer that supports common APIs for machine learning and AI tools to automatically detect performance anomalies, determine the root cause and take the necessary remedial actions.
“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.
The ONUG AIOps Working Group will discuss and demonstrate the progress of the framework on Thursday, October 17, 9:40 am at the Metropolitan Pavilion in New York City.
“We welcome all enterprise users and vendors to join the ONUG Community and participate in the AIOps for Hybrid Multi-Cloud Working Group,” said Stephen Collins, ONUG Working Group CTO. “The demonstration in New York is but the first step on the long road to fully leveraging the power of AI to streamline and automate IT operations in the hybrid multi-cloud era. The working group is keen to see users and vendors collaborating on multiple pilot proof-of-concept demonstrations that incorporate live data sourced from real-world operational environments.”
The test environment for the AIOps proof of concept was provided by an end-user IT group at the Orlando VA Medical Center.
To learn more, visit ONUG Fall 2019 on October 16-17 in New York City. Accredited media are encouraged to attend. To request a press pass please click here.
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ONUG is the only organization composed of senior-level IT executives from the Global 2000 that represents the interest and initiatives of the Enterprise Community. Through our global event series, Working Groups, training academies, and webinars, ONUG plays a central role in the creation of new and improved tools to develop, manage, and secure the Digital Enterprise. ONUG’s peer permission structure fosters the exchange of information among the world’s largest organizations as they build and secure the digital economy. The ONUG Board is comprised of IT leaders from Bank of America, Cigna, Citigroup, Credit Suisse, eBay, FedEx, Fidelity Investments, Gap Inc., GE, Intuit, JP Morgan Chase, Kaiser Permanente, the Lippis Report, Morgan Stanley, Pfizer, State Street Bank, TD Ameritrade, UBS, Oath, and hundreds more. For more on ONUG, go to www.onug.net or follow us on Twitter @ONUG_.
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About Mist Systems
Mist Systems, a Juniper Networks company, is leading the transition to AI-driven IT. The Mist Learning Wireless LAN (WLAN) makes Wi-Fi predictable, reliable and measurable by providing unprecedented visibility into the user experience and by replacing time-consuming manual IT tasks with proactive automation. In addition, Mist brings enterprise-grade Wi-Fi, BLE and IoT together to deliver personalized, location-based wireless services without requiring battery-powered beacons. All operations are managed via Mist’s modern cloud architecture for maximum scalability, agility and performance.
AtScale is the category creator and leading provider of adaptive analytics for data architecture modernization. Enterprises worldwide use AtScale to automate their data engineering, eliminating data silos without the need for additional investments in data engineering. AtScale provides rich, atomic-level data access to the enterprise to empower their data science and analytics teams.
AtScale connects people to live data without the need to move it and without the traditional complexity — leveraging existing investments in big data platforms, applications, and tools. AtScale creates a single set of semantics so consumers can query live data (either on-premise or in the cloud) in seconds without having to understand how or where it is stored — with security, governance, and predictability in data usage and storage costs.
By making data consumers more productive and enabling the unification of data, AtScale helps organizations transform into insight-driven enterprises. With offices around the globe, AtScale is backed by top-tier investors, including Morgan Stanley, Storm Ventures, Atlantic Bridge and Wells Fargo. For more information, visit www.AtScale.com, and join the conversation on Twitter and LinkedIn.