USENIX ATC '24 and OSDI '24

Qingyuan Liu, Yanning Yang, Dong Du, and Yubin Xia, Institute of Parallel and Distributed Systems, SEIEE, Shanghai Jiao Tong University; Engineering Research Center for Domain-specific Operating Systems, Ministry of Education; Ping Zhang and Jia Feng, Huawei Cloud; James R. Larus, EPFL; Haibo Chen, Institute of Parallel and Distributed Systems, SEIEE, Shanghai Jiao Tong University; Engineering Research Center for Domain-specific Operating Systems, Ministry of Education; Key Laboratory of System Software (Chinese Academy of Science)

Current serverless platforms struggle to optimize resource utilization due to their dynamic and fine-grained nature. Conventional techniques like overcommitment and autoscaling fall short, often sacrificing utilization for practicability or incurring performance trade-offs. Overcommitment requires predicting performance to prevent QoS violation, introducing trade-off between prediction accuracy and overheads. Autoscaling requires scaling instances in response to load fluctuations quickly to reduce resource wastage, but more frequent scaling also leads to more cold start overheads. This paper introduces Jiagu to harmonize efficiency with practicability through two novel techniques. First, pre-decision scheduling achieves accurate prediction while eliminating overheads by decoupling prediction and scheduling. Second, \emph{dual-staged scaling} achieves frequent adjustment of instances with minimum overhead. We have implemented a prototype and evaluated it using real-world applications and traces from the public cloud platform. Our evaluation shows a 54.8% improvement in deployment density over commercial clouds (with Kubernetes) while maintaining QoS, and 81.0%–93.7% lower scheduling costs and a 57.4%–69.3% reduction in cold start latency compared to existing QoS-aware schedulers.

https://www.usenix.org/conference/atc24/presentation/liu-qingyuan