Translation Pass-Through for Near-Native Paging Performance in VMs

Shai Bergman, Mark Silberstein, Peter Pietzuch, Takahiro Shinagawa, Lluís Vilanova

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Virtual machines (VMs) are used for consolidation, isolation, and provisioning in the cloud, but applications with large working sets are impacted by the overheads of memory address translation in VMs. Existing translation approaches incur non-trivial overheads: (i) nested paging has a worst-case latency that increases with page table depth; and (ii) paravirtualized and shadow paging suffer from high hypervisor intervention costs when updating guest page tables. We describe translation pass-through (TPT), a new memory virtualization mechanism that achieves near-native performance. TPT enables VMs to control virtual memory translation from guest-virtual to host-physical addresses using one-dimensional page tables. At the same time, inter-VM isolation is enforced by the host by exploiting new hardware support for physical memory tagging in commodity CPUs. We prototype TPT by modifying the KVM/QEMU hypervisor and enlightening the Linux guest. We evaluate it by emulating the memory tagging mechanism of AMD CPUs. Our conservative performance estimates show that TPT achieves native performance for real-world data center applications, with speedups of up to 2.4× and 1.4× over nested and shadow paging, respectively.

Original languageEnglish
Title of host publicationProceedings of the 2023 USENIX Annual Technical Conference, ATC 2023
Pages753-768
Number of pages16
ISBN (Electronic)9781939133359
StatePublished - 2023
Event2023 USENIX Annual Technical Conference, ATC 2023 - Boston, United States
Duration: 10 Jul 202312 Jul 2023

Publication series

NameProceedings of the 2023 USENIX Annual Technical Conference, ATC 2023

Conference

Conference2023 USENIX Annual Technical Conference, ATC 2023
Country/TerritoryUnited States
CityBoston
Period10/07/2312/07/23

All Science Journal Classification (ASJC) codes

  • General Computer Science

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