Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Dong Liu 0042

dblp:98/1737-42 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0009-3181-5994ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Memory systems · 56% Cloud and datacenter computing · 44%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Memory systems › memory management › virtual memory
huge pages
1.012026
A Comprehensive Study on Solving Memory Bloat Under Virtualization · ACM Trans. Comput. Syst. 2026
Cloud and datacenter computing › virtualization › virtual machine memory management
hypervisor memory management
1.012026
A Comprehensive Study on Solving Memory Bloat Under Virtualization · ACM Trans. Comput. Syst. 2026
Cloud and datacenter computing
virtualization
1.012026
A Comprehensive Study on Solving Memory Bloat Under Virtualization · ACM Trans. Comput. Syst. 2026
Memory systems › memory management
virtual memory
1.012026
A Comprehensive Study on Solving Memory Bloat Under Virtualization · ACM Trans. Comput. Syst. 2026
Memory systems › tiered memory
tiered memory management
0.312026
A Comprehensive Study on Solving Memory Bloat Under Virtualization · ACM Trans. Comput. Syst. 2026

Methods — techniques the papers use, named apart from their topics

page tracking · 1.0indirect memory translation · 1.0
YearPublicationVenuePosition
2026 A Comprehensive Study on Solving Memory Bloat Under Virtualization
abstract
Huge pages are effective in reducing address translation overhead under virtualization. However, huge pages can lead to the memory bloat problem, which manifests in two primary forms: hot bloat and usage bloat . Hot bloat occurs when accesses to a huge page are heavily skewed towards a small subset of base pages, leading the hypervisor to (mistakenly) classify the entire huge page as hot. Hot bloat undermines several critical virtualization techniques, including tiered memory and page sharing. Usage bloatrefers to the base pages within a huge page that has not yet been allocated, causing virtual machines (VMs) to demand excessive memory. Prior work addressing memory bloat either requires hardware modification or targets a specific scenario and is not applicable to a hypervisor. This article presents HugeScope , a lightweight, effective and generic system that addresses the memory 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. It can not only be easily applied to various scenarios concerning hot bloat , including tiered memory management ( HS-TMM ) and page sharing ( HS-Share ), but also seamlessly expose its capabilities to VMs to address the usage bloat problem ( HS-HP ). Evaluation shows that HugeScope incurs less than 4% overhead, 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, and By addressing usage bloat , HS-HP can eliminate excessive memory usage, and achieve performance improvements of up to 11% over HawkEye.
Chuandong Li 0004, Dong Liu 0042, Zhihong Xue, Xiaolin Wang 0001, Zhenlin Wang 0003, Yingwei Luo, Diyu Zhou
ACM Trans. Comput. Syst.3