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Title: Argus: Debugging Performance Issues in Modern Desktop Applications with Annotated Causal Tracing
Modern desktop applications involve many asynchronous, concurrent interactions that make performance issues difficult to diagnose. Although prior work has used causal tracing for debugging performance issues in distributed systems, we find that these techniques suffer from high inaccuracies for desktop applications. We present Argus, a fast, effective causal tracing tool for debugging performance anomalies in desktop applications. Argus introduces a novel notion of strong and weak edges to explicitly model and annotate trace graph ambiguities, a new beam-search-based diagnosis algorithm to select the most likely causal paths in the presence of ambiguities, and a new way to compare causal paths across normal and abnormal executions. We have implemented Argus across multiple versions of macOS and evaluated it on 12 infamous spinning pinwheel issues in popular macOS applications. Argus diagnosed the root causes for all issues, 10 of which were previously unknown, some of which have been open for several years. Argus incurs less than 5% CPU overhead when its system-wide tracing is enabled, making always-on tracing feasible.  more » « less
Award ID(s):
1910133
PAR ID:
10283348
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
The 2021 USENIX Annual Technical Conference
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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