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TraStrainer: Adaptive Sampling for Distributed Traces with System Runtime State.

, , , , , , , and . Proc. ACM Softw. Eng., 1 (FSE): 473-493 (2024)

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LogReducer: Identify and Reduce Log Hotspots in Kernel on the Fly., , , , , , and . ICSE, page 1763-1775. IEEE, (2023)T-Rank: A Lightweight Spectrum based Fault Localization Approach for Microservice Systems., , and . CCGRID, page 416-425. IEEE, (2021)TraStrainer: Adaptive Sampling for Distributed Traces with System Runtime State., , , , , , , and . Proc. ACM Softw. Eng., 1 (FSE): 473-493 (2024)Nezha: Interpretable Fine-Grained Root Causes Analysis for Microservices on Multi-modal Observability Data., , , , , and . ESEC/SIGSOFT FSE, page 553-565. ACM, (2023)MicroRank: End-to-End Latency Issue Localization with Extended Spectrum Analysis in Microservice Environments., , , , , , , , and . WWW, page 3087-3098. ACM / IW3C2, (2021)MicroSketch: Lightweight and Adaptive Sketch Based Performance Issue Detection and Localization in Microservice Systems., , , , and . ICSOC, volume 13740 of Lecture Notes in Computer Science, page 219-236. Springer, (2022)Going through the Life Cycle of Faults in Clouds: Guidelines on Fault Handling., , , , and . ISSRE, page 121-132. IEEE, (2022)MicroFI: Non-Intrusive and Prioritized Request-Level Fault Injection for Microservice Applications., , , , and . IEEE Trans. Dependable Secur. Comput., 21 (5): 4921-4938 (September 2024)Microscaler: Automatic Scaling for Microservices with an Online Learning Approach., , and . ICWS, page 68-75. IEEE, (2019)Sieve: Attention-based Sampling of End-to-End Trace Data in Distributed Microservice Systems., , , , and . ICWS, page 436-446. IEEE, (2021)