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Syst."],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:p>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.<\/jats:p>\n                  <jats:p>\n                    This article presents\n                    <jats:sc>HugeScope<\/jats:sc>\n                    , a lightweight, effective and generic system that addresses the memory bloat problem under virtualization based on commodity hardware.\n                    <jats:sc>HugeScope<\/jats:sc>\n                    includes an efficient and precise page tracking mechanism, leveraging the other level of indirect memory translation in the hypervisor.\n                    <jats:sc>HugeScope<\/jats:sc>\n                    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,\n                    <jats:sc>HugeScope<\/jats:sc>\n                    is general and modular. It can not only be easily applied to various scenarios concerning hot bloat , including tiered memory management (\n                    <jats:sc>HS-TMM<\/jats:sc>\n                    ) and page sharing (\n                    <jats:sc>HS-Share<\/jats:sc>\n                    ), but also seamlessly expose its capabilities to VMs to address the usage bloat problem (\n                    <jats:sc>HS-HP<\/jats:sc>\n                    ). Evaluation shows that\n                    <jats:sc>HugeScope<\/jats:sc>\n                    incurs less than 4% overhead, by addressing hot bloat ,\n                    <jats:sc>HS-TMM<\/jats:sc>\n                    improves performance by up to 61% over vTMM while\n                    <jats:sc>HS-Share<\/jats:sc>\n                    saves 41% more memory than Ingens while offering comparable performance, and By addressing usage bloat ,\n                    <jats:sc>HS-HP<\/jats:sc>\n                    can eliminate excessive memory usage, and achieve performance improvements of up to 11% over HawkEye.\n                  <\/jats:p>","DOI":"10.1145\/3769429","type":"journal-article","created":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T11:28:49Z","timestamp":1759922929000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Comprehensive Study on Solving Memory Bloat Under Virtualization"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7449-5105","authenticated-orcid":false,"given":"Chuandong","family":"Li","sequence":"first","affiliation":[{"name":"Peking University","place":["Beijing, China"]},{"name":"Zhongguancun Laboratory","place":["Beijing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0449-6581","authenticated-orcid":false,"given":"Zhe","family":"Tang","sequence":"additional","affiliation":[{"name":"Peking University","place":["Beijing, China"]},{"name":"Zhongguancun Laboratory","place":["Beijing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3181-5994","authenticated-orcid":false,"given":"Dong","family":"Liu","sequence":"additional","affiliation":[{"name":"Center Research Institute, ZTE Coporation","place":["Chengdu, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5735-2498","authenticated-orcid":false,"given":"Zhihong","family":"Xue","sequence":"additional","affiliation":[{"name":"Center Research Institute, ZTE Coporation","place":["Chengdu, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6951-1613","authenticated-orcid":false,"given":"Xiaolin","family":"Wang","sequence":"additional","affiliation":[{"name":"Computer Science, Peking University","place":["Beijing, China"]},{"name":"Zhongguancun Laboratory","place":["Beijing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0429-4371","authenticated-orcid":false,"given":"Zhenlin","family":"Wang","sequence":"additional","affiliation":[{"name":"Computer Science, Michigan Technological University","place":["Houghton, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7903-0717","authenticated-orcid":false,"given":"Yingwei","family":"Luo","sequence":"additional","affiliation":[{"name":"Computer Science, Peking University","place":["Beijing, China"]},{"name":"Zhongguancun Laboratory","place":["Beijing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8620-1064","authenticated-orcid":false,"given":"Diyu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Peking University","place":["Beijing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,11,7]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"MongoDB administration documentation. 2023. 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