Jiagu: Optimizing Serverless Computing Resource Utilization with Harmonized Efficiency and Practicability
arxiv(2024)
摘要
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, which harmonizes efficiency with practicability through two novel
techniques. First, pre-decision scheduling achieves accurate prediction while
eliminating overheads by decoupling prediction and scheduling. Second,
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
Kubernetes) while maintaining QoS, and 81.0
a 57.4
schedulers in research work.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要