New working group debuting at ONUG Fall 2019
An exploratory conference session on AI IT operations at ONUG Spring 2019 validated that the ONUG Community is eager to tackle the application of AI technology to streamline operational workflows and automate critical operations in hybrid multi-cloud environments where scale and complexity are outstripping the capabilities of human operators to keep pace in real time. As a result, a new AIOps for Hybrid Multi-Cloud Working Group will be making its debut at ONUG Fall 2019.
The working group will concentrate on the challenges of applying AI techniques to generate actionable insights by using machine learning to analyze and correlate data that is sourced from operational silos across multiple operational domains and layers – application, infrastructure and network.
One approach is to provide an abstraction layer that provides APIs for accessing a virtual data lake that spans multiple monitoring data repositories. A cloud-based virtual data lake simplifies the process of aggregating data from multiple operational silos. Data records should adhere to a common format or schema that supports the necessary fields for tagging and enriching data in order to correlate metrics, logs and traces spanning multiple silos.
The virtual data lake construct allows machine intelligence to be applied within a specific operational domain or across multiple domains. AI and machine learning algorithms analyze and correlate monitoring data to generate actionable insights for use cases such as root cause analysis, performance monitoring, security and predictive analytics.
The initial focus for the working group will be to create a pilot proof-of-concept demonstration that will be based on a real-world, cloud-based SaaS application utilizing monitoring data collected across multiple domains and stored in public cloud repositories. AI-powered applications used by various operations teams will utilize data stored in these repositories by accessing a data virtualization layer that provides a unified data model which simplifies access to data collected across multiple operational silos.
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