Exploiting hardware performance counters with flow and context sensitive profiling
Proceedings of the ACM SIGPLAN 1997 conference on Programming language design and implementation
Stardust: tracking activity in a distributed storage system
SIGMETRICS '06/Performance '06 Proceedings of the joint international conference on Measurement and modeling of computer systems
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Ursa minor: versatile cluster-based storage
FAST'05 Proceedings of the 4th conference on USENIX Conference on File and Storage Technologies - Volume 4
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Using magpie for request extraction and workload modelling
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Pip: detecting the unexpected in distributed systems
NSDI'06 Proceedings of the 3rd conference on Networked Systems Design & Implementation - Volume 3
Discovery of application workloads from network file traces
FAST'10 Proceedings of the 8th USENIX conference on File and storage technologies
CLUEBOX: a performance log analyzer for automated troubleshooting
WASL'08 Proceedings of the First USENIX conference on Analysis of system logs
Diagnosing performance changes by comparing request flows
Proceedings of the 8th USENIX conference on Networked systems design and implementation
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Making request flow tracing an integral part of software systems creates the potential to better understand their operation. The resulting traces can be converted to per-request graphs of the work performed by a service, representing the flow and timing of each request's processing. Collectively, these graphs contain detailed and comprehensive data about the system's behavior and the workload that induced it, leaving the challenge of extracting insights. Categorizing and differencing such graphs should greatly improve our ability to understand the runtime behavior of complex distributed services and diagnose problems. Clustering the set of graphs can identify common request processing paths and expose outliers. Moreover, clustering two sets of graphs can expose differences between the two; for example, a programmer could diagnose a problem that arises by comparing current request processing with that of an earlier non-problem period and focusing on the aspects that change. Such categorizing and differencing of system behavior can be a big step in the direction of automated problem diagnosis.