AI for Cloud Ops

Cross-layer data visualization that will be designed as part of the project. Each geometric shape in the trace graph represents a cloud entity, such as a container, service, load balancer, etc.
  • BU Faculty Members: Ayse Coskun, Alan Liu, and Gianluca Stringhini
  • Red Hatters: Steven Huels, Marcel Hild, and Daniel Riek
  • IBM researcher: Fabio Oliviera
  • Graduate Students: Anthony Byrne, Mert Toslali, Saad Ullah, and Lesley Zhou

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 break-neck 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.

Project Repositories

Iter8 Online Experimentation Framework

Praxi Software Discovery using ML

ACE: Approximate Concrete Execution 

Other Funding

Ayse Coskun, IBM Faculty Award, 2020
Ayse Coskun (Co-PI), NSF CISE CSR, A Just-in-Time, Cross-Layer Instrumentation Framework for Diagnosing Performance Problems in Distributed Applications. PI: Raja Sambasivan at Tufts University, 2018-2022
Ayse Coskun, IBM Open Collaborative Research Award, 2016-2020
Ayse Coskun, Red Hat Collaboratory, 2018-2020

Check out more about this project on the Red Hat Research website.