• DocumentCode
    154152
  • Title

    Xentry: Hypervisor-Level Soft Error Detection

  • Author

    Xin Xu ; Chiang, Ron C. ; Huang, He Helen

  • fYear
    2014
  • fDate
    9-12 Sept. 2014
  • Firstpage
    341
  • Lastpage
    350
  • Abstract
    Cloud data centers leverage virtualization to share commodity hardware resources, where virtual machines (VMs) achieve fault isolation by containing VM failures within the virtualization boundary. However, hypervisor failure induced by soft errors will most likely affect multiple, if not all, VMs on a single physical host. Existing fault detection techniques are not well equipped to handle such hypervisor failures. In this paper, we propose a new soft error detection framework, Xentry (a sentry on soft error for Xen), that focuses on limiting error propagation within and from the hypervisor. In particular, we have designed a VM transition detection technique to identify incorrect control flow before VM execution resumes, and a runtime detection technique to shorten detection latency. This framework requires no hardware modification and has been implemented in the Xen hypervisor. The experiment results show that Xentry incurs very small performance overhead and detects over 99% of the injected faults.
  • Keywords
    error detection; virtual machines; virtualisation; VM execution; VM failures; VM transition detection; Xen hypervisor; Xentry; cloud data centers leverage virtualization; commodity hardware resources; control flow; detection latency; error propagation; fault detection techniques; fault isolation; hardware modification; hypervisor failures; hypervisor-level soft error detection framework; runtime detection; virtual machines; virtualization boundary; Context; Decision trees; Hardware; Runtime; Software; Virtual machine monitors; Virtualization; Error detection; Hypervisor; Virtualization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Parallel Processing (ICPP), 2014 43rd International Conference on
  • Conference_Location
    Minneapolis MN
  • ISSN
    0190-3918
  • Type

    conf

  • DOI
    10.1109/ICPP.2014.43
  • Filename
    6957243