ML-Powered Problem Detection in Chameleon

Hello, I am Syed Mohammad Qasim, a PhD candidate in Electrical and Computer Engineering at Boston University. I will be spending my summer working on the project ML-Powered Problem Detection in Chameleon under the mentorship of Ayse Coskun and Michael Sherman.

Currently, Chameleon Cloud monitors sites at the Texas Advanced Computing Center (TACC), University of Chicago, Northwestern University, and Argonne National Lab. They collect metrics using Prometheus at each site and feed them all to a central Mimir cluster. All the logs go to a central Loki, and Grafana is used to visualize and set alerts. Chameleon currently collects around 3000 metrics. Manually reviewing and setting alerts on them is time-consuming and labor-intensive. This project aims to help Chameleon operators monitor their systems more effectively and improve overall reliability by creating an anomaly detection service that can augment the existing alerting framework.

Syed Mohammad Qasim
Syed Mohammad Qasim
PhD Candidate at Boston University

Syed Mohammad Qasim is a PhD student specializing in performance debugging and anomaly detection in cloud computing environments.