USENIX ATC '24 and OSDI '24

Chuandong Li, National Key Laboratory for Multimedia Information Processing, School of CS, Peking University and Zhongguancun Laboratory; Sai Sha, National Key Laboratory for Multimedia Information Processing, School of CS, Peking University and Beijing Huawei Digital Technologies; Yangqing Zeng and Xiran Yang, National Key Laboratory for Multimedia Information Processing, School of CS, Peking University; Yingwei Luo and Xiaolin Wang, National Key Laboratory for Multimedia Information Processing, School of CS, Peking University and Zhongguancun Laboratory; Zhenlin Wang, Michigan Tech; Diyu Zhou, National Key Laboratory for Multimedia Information Processing, School of CS, Peking University and EPFL

Huge pages are effective in reducing the address translation overhead under virtualization. However, huge pages suffer from the hot bloat problem, where accesses to a huge page are skewed towards a few base pages (i.e., 4KB page), making the hypervisor (mistakenly) classify the whole huge page as hot. Hot bloat renders several critical techniques used in virtualization ineffective, including tiered memory and page sharing. Prior work addressing hot bloat either requires hardware modification or targets a specific scenario and is not applicable to a hypervisor.

This paper presents HugeScope, a lightweight, effective and generic system that addresses the hot bloat problem under virtualization based on commodity hardware. HugeScope includes an efficient and precise page tracking mechanism, leveraging the other level of indirect memory translation in the hypervisor. HugeScope provides a generic framework to support page splitting and coalescing policies, considering the memory pressure, as well as the recency, frequency, and skewness of page access. Moreover, HugeScope is general and modular, thereby can be easily applied to various scenarios concerning hot bloat, including tiered memory management (HS-TMM) and page sharing (HS-Share). Evaluation shows that HugeScope incurs less than 4% overhead, and by addressing hot bloat, HS-TMM improves performance by up to 61% over vTMM while HS-Share saves 41% more memory than Ingens while offering comparable performance.

https://www.usenix.org/conference/atc24/presentation/li-chuandong