CZone: memory-efficient serverless container management with MCPV model

Chenyang WU , Zijun LI , Chuhao XU , Quan CHEN , Minyi GUO

Front. Comput. Sci. ›› 2026, Vol. 20 ›› Issue (5) : 2005105

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Front. Comput. Sci. ›› 2026, Vol. 20 ›› Issue (5) : 2005105 DOI: 10.1007/s11704-024-40756-5
Architecture
RESEARCH ARTICLE

CZone: memory-efficient serverless container management with MCPV model

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Abstract

Serverless computing usually employs secure containers, which are encapsulated within lightweight microVMs, to isolate function invocations across different tenants. For high security guarantees, such Single-Container-Per-VM (SCPV) model results in large memory waste, as each microVM includes a guestOS, even though they are identical. For memory efficiency of the secure container architecture, we advocate for the dynamic Multi-Containers-Per-VM (MCPV) model. Adopting the MCPV model, a microVM can accommodate multiple containers for the same functions, and the microVM’s memory space adjusts dynamically based on workload.

However, implementing this model necessitates efficient memory hot-plug and hot-unplug techniques. Existing methods either significantly impair function performance within containers or fail to adequately unplug all required pages. To address this challenge, we propose CZone, a dedicated memory hot-plug and hot-unplug design specifically tailored to support the dynamic MCPV model. CZone ensures that the used memory pages of a container are located in contiguous physical memory regions, with each region exclusively allocated to a single container. Experimental results demonstrate that MCPV with CZone brings an 81.81% reduction in startup latency and an 89.87% reduction in memory footprint when compared to the existing SCPV model that utilizes microVM templating, with negligible system performance impact.

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serverless / function-as-a-service / secure container runtime / memory hot-plug

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Chenyang WU, Zijun LI, Chuhao XU, Quan CHEN, Minyi GUO. CZone: memory-efficient serverless container management with MCPV model. Front. Comput. Sci., 2026, 20(5): 2005105 DOI:10.1007/s11704-024-40756-5

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