Advancing State-of-the-art Log Analytics Infrastructures
Abstract
Software systems usually record important runtime information in their logs. Logs help practitioners understand system runtime behaviors and diagnose field failures. As logs are usually very large in size, automated log analysis is needed to assist practitioners in their software operation and maintenance efforts. The success of adopting log analysis in practice often depends on sophisticated infrastructures. In particular, to enable fast queries and to save storage space, such infrastructure split log data into small blocks (e.g., 16KB), then index and compress each block separately. Afterwards a log parsing step converts the raw logs from unstructured text to a structured format before applying subsequent log analysis techniques. My lab specializes in the development of approaches for advancing the infractures of log analysis. In this talk, I provide an overview of some of our recent work on automated techniques of log compression and parsing.
Weiyi Shang
Associate Professor of Software Engineering at Concordia University
Weiyi Shang is a Concordia University Research Chair at the Department of Computer Science. His research interests include AIOps, big bata software engineering, software log analytics and software performance engineering. He serves as a Steering committee member of the SPEC Research Group. He is ranked among the top worldwide established SE research stars in a recent bibliometrics assessment of software engineering scholars. He is a recipient of various premium awards, including the CSCAN Outstanding Early-Career Computer Science Researcher Prize, the SIGSOFT Distinguished paper award at ICSE 2013, best paper award at WCRE 2011 and the Distinguished reviewer award for the Empirical Software Engineering journal. His research has been adopted by industrial collaborators (e.g., BlackBerry and Ericsson) to improve the quality and performance of their software systems that are used by millions of users worldwide.