PVM: Efficient Shadow Paging for Deploying Secure Containers in Cloud-native Environments

被引:1
|
作者
Huang, Hang [1 ]
Lai, Jiangshan [2 ]
Rao, Jia [3 ]
Lu, Hui [3 ]
Hou, Wenlong [2 ]
Su, Hang [2 ]
Xu, Quan [1 ]
Zhong, Jiang [1 ]
Zeng, Jiahao [1 ]
Wang, Xu [2 ]
He, Zhengyu [2 ]
Han, Weidong [1 ]
Liu, Jiang [1 ]
Ma, Tao [1 ]
Wu, Song [4 ]
机构
[1] Alibaba Grp, Hangzhou, Peoples R China
[2] Ant Grp, Hangzhou, Peoples R China
[3] Univ Texas Arlington, Arlington, TX USA
[4] Huazhong Univ Sci & Technol, Wuhan, Peoples R China
基金
美国国家科学基金会;
关键词
D O I
10.1145/3600006.3613158
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
In cloud-native environments, containers are often deployed within lightweight virtual machines (VMs) to ensure strong security isolation and privacy protection. With the growing demand for customized cloud services, third-party vendors are turning to infrastructure-as-a-service (IaaS) cloud providers to build their own cloud-native platforms, necessitating the need to run a VM or a guest that hosts containers inside another VM instance leased from an IaaS cloud. State-of-the-art nested virtualization in the x86 architecture relies heavily on the host hypervisor to expose hardware virtualization support to the guest hypervisor, not only complicating cloud management but also raising concerns about an increased attack surface at the host hypervisor. This paper presents the design and implementation of PVM, a high-performance guest hypervisor for KVM that is transparent to the host hypervisor and assumes no hardware virtualization support. PVM leverages two key designs: 1) a minimal shared memory region between the guest and guest hypervisor to facilitate state transition between different privilege levels and 2) an efficient shadow page table design to reduce the cost of memory virtualization. PVM has been adopted by Alibaba Cloud for hosting tens of thousands of secure containers on a daily basis. Our experiments demonstrate that PVM significantly outperforms current nested virtualization in KVM for memory virtualization, particularly for concurrent workloads, while maintaining comparable performance in CPU and I/O virtualization.
引用
收藏
页码:515 / 530
页数:16
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