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Autothrottle: Resource Management for SLO-Targeted Microservices
Zibo Wang, University of Science and Technology of China and Microsoft Research; Pinghe Li, ETH Zurich; Chieh-Jan Mike Liang, Microsoft Research; Feng Wu, University of Science and Technology of China; Francis Y. Yan, Microsoft Research Awarded Outstanding Paper! Achieving resource efficiency while preserving end-user experience is non-trivial for cloud application operators.
@inproceedings {295485, author = {Zibo Wang and Pinghe Li and Chieh-Jan Mike Liang and Feng Wu and Francis Y. Yan}, title = {Autothrottle: A Practical {Bi-Level} Approach to Resource Management for {SLO-Targeted} Microservices}, booktitle = {21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24)}, year = {2024}, isbn = {978-1-939133-39-7}, address = {Santa Clara, CA}, pages = {149--165}, url = {https://www.usenix.org/conference/nsdi24/presentation/wang-zibo}, publisher = {USENIX Association}, month = apr } Translating between the two levels, however, is challenging because user requests traverse heterogeneous services that collectively (but unevenly) contribute to the end-to-end latency. It architecturally decouples application SLO feedback from service resource control, and bridges them through the notion of performance targets.
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