Uploaded February 2022 | Updated September 2026, 22 minutes ago
Today’s Continuous Integration/Continuous Development (CI/CD) trends encourage rapid design of software using a wide range of customized, off-the-shelf, and legacy software components, followed by frequent updates that are immediately deployed on the cloud. Altogether, this component diversity and breakneck pace of development amplify the difficulty in identifying, localizing, or fixing problems related to performance, resilience, and security. Existing approaches that rely on human experts have limited applicability to modern CI/CD processes, as they are fragile, costly, and often not scalable. This project aims to address this gap in effective cloud management and operations with a concerted, systematic approach to building and integrating AI-driven software analytics into production systems. We aim to provide a rich selection of heavily-automated “ops” functionality as well as intuitive, easily-accessible analytics to users, developers, and administrators. In this way, our longer-term aim is to improve performance, resilience, and security in the cloud without incurring high operation costs.
Please visit the AI for Cloud Ops Project page at
research.redhat.com/blog/research_project/ai-for-cloud-ops
to learn more.
Speakers:
Ayse Coskun, Boston University
Gianluca Stringhini, Boston University
Alan Liu, Boston University
Marcel Hild, Red Hat
Conversation Leader:
Michael Clifford, Red Hat
Today’s Continuous Integration/Continuous Development (CI/CD) trends encourage rapid design of software using a wide range of customized, off-the-shelf, and legacy software components, followed by frequent updates that are immediately deployed on the cloud. Altogether, this component diversity and breakneck pace of development amplify the difficulty in identifying, localizing, or fixing problems related to performance, resilience, and security. Existing approaches that rely on human experts have limited applicability to modern CI/CD processes, as they are fragile, costly, and often not scalable. This project aims to address this gap in effective cloud management and operations with a concerted, systematic approach to building and integrating AI-driven software analytics into production systems. We aim to provide a rich selection of heavily-automated “ops” functionality as well as intuitive, easily-accessible analytics to users, developers, and administrators. In this way, our longer-term aim is to improve performance, resilience, and security in the cloud without incurring high operation costs.
Please visit the AI for Cloud Ops Project page at
research.redhat.com/blog/research_project/ai-for-cloud-ops
to learn more.
Speakers:
Ayse Coskun, Boston University
Gianluca Stringhini, Boston University
Alan Liu, Boston University
Marcel Hild, Red Hat
Conversation Leader:
Michael Clifford, Red Hat
