Virtual Machine NUMA Placement at Scale: Learning the Norm, Shielding the Tail

Yibo Zhao, Tianyuan Wu, Hui Xue, Qi Chen, Zhenhua Han, Zikai Xu, Yuntai Chang, Rui Gao, Steve Deng, Ray Jui-Hao Chiang, Mingxia Li, Yuqing Yang, Cheng Tan, Fan Yang, Peng Cheng, Yongqiang Xiong, Lili Qiu, Lidong Zhou

Abstract:

In modern data centers, servers organize memory and CPUs into Non-Uniform Memory Access (NUMA) nodes,
where unequal memory-to-CPU proximity leads to varying memory latency. Hypervisors must carefully place
Virtual Machines (VMs) to reduce remote memory access. Poor placements can lead to significant performance
degradation—sometimes up to 30%. However, achieving optimal placement at scale is challenging due to the
large number of VM configurations, diverse NUMA structures, and evolving workload patterns. We present
Catur, a NUMA placement system designed for large-scale cloud environments. Catur leverages reinforcement
learning to learn from production data. Moreover, to address real-world challenges, Catur integrates several techniques: robust action space design to prevent model collapse, reward shaping to address learning inefficiency,
drift-aware continuous training for evolving workload patterns, and speculative shielding to mitigate VM performance anomalies. Evaluations on production traces with 100 million VMs demonstrate that Catur reduces
average resource defect by 34.2%–50.0% compared to state-of-the-art hypervisor policies.

Topics: ML for Systems: ML for systems infrastructure

Submission Number: 64