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(ICWS 2020) Finding Performance Patterns from Logs with High Confidence

About This Webinar

Abstract: Performance logs contain rich information about a system's state. Large-scale web service infrastructures deployed in the cloud are notoriously difficult to troubleshoot, especially performance bugs. Detecting, isolating and diagnosing fine-grained performance anomalies requires integrating system performance measures across space and time. To achieve scale, we present our megatables approach, which automatically interprets performance log data and outputs millibottleneck predictions along with supporting visualizations. We evaluate our method with three illustrative scenarios, and we assess its predictive ability. We also evaluate its ability to extract meaningful information from many log samples drawn from the wild.

Authors: Joshua Kimball, Rodrigo Alves Lima and Calton Pu (Georgia Institute of Technology, USA)

Email: jmkimball@gatech.edu, ral@gatech.edu, calton@cc.gatech.edu

Who can view: Everyone
Webinar Price: Free
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Joshua Kimball is in the final year of his PhD in Computer Science at Georgia Institute of Technology, which he completed while working as the Chief Data Scientist for a local AdTech startup. His research lies at the intersection of Systems and Database with an emphasis on data management and applied machine learning. Prior to Georgia Tech, he completed a Masters at Georgia Tech and built financial derivative processing systems at Bank of New York Mellon. He complete his undergraduate studies at Carnegie Mellon University.
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