A VMM-level approach to shortening downtime of operating systems reboots in software updates

Hiroshi Yamada, Kenji Kono

研究成果: Article査読

抄録

Operating system (OS) reboots are an essential part of updating kernels and applications on laptops and desktop PCs. Long downtime during OS reboots severely disrupts users' computational activities. This long disruption discourages the users from conducting OS reboots, failing to enforce them to conduct software updates. Although the dynamic updatable techniques have been widely studied, making the system "reboot-free" is still difficult due to their several limitations. As a result, users cannot benefit from new functionality or better performance, and even worse, unfixed vulnerabilities can be exploited by attackers. This paper presents ShadowReboot, a virtual machine monitor (VMM)-based approach that shortens downtime of OS reboots in software updates. ShadowReboot conceals OS reboot activities from user's applications by spawning a VM dedicated to an OS reboot and systematically producing the rebooted state where the updated kernel and applications are ready for use. ShadowReboot provides an illusion to the users that the guest OS travels forward in time to the rebooted state. ShadowReboot offers the following advantages. It can be used to apply patches to the kernels and even system configuration updates. Next, it does not require any special patch requiring detailed knowledge about the target kernels. Lastly, it does not require any target kernel modification. We implemented a prototype in VirtualBox 4.0.10 OSE. Our experimental results show that ShadowReboot successfully updated software on unmodified commodity OS kernels and shortened the downtime of commodity OS reboots on five Linux distributions (Fedora, Ubuntu, Gentoo, Cent, and SUSE) by 91 to 98%.

本文言語English
ページ(範囲)2663-2675
ページ数13
ジャーナルIEICE Transactions on Information and Systems
E97D
10
DOI
出版ステータスPublished - 2014 10 1

ASJC Scopus subject areas

  • ソフトウェア
  • ハードウェアとアーキテクチャ
  • コンピュータ ビジョンおよびパターン認識
  • 電子工学および電気工学
  • 人工知能

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