Performance Evaluation of a Xen-based Virtual Environment for High Performance Computing Systems

  • Vaddadi P. Chandu
  • Karandeep Singh


Virtualization is becoming an increasingly popular method to achieve software-based solution for sharing hardware infrastructure in a completely isolated manner. Xen virtual machine monitor (an open source software) is becoming a popular resource to implement virtualization for managing multiple operating systems instances within one physical computing node. Current research has focused on migrating these instances between nodes during runtime. This capability is useful for numerous activities, such as fault management, load balancing, and low-level system maintenance. In this paper, we evaluate the performance of Xen Virtual Machine Monitor for high performance computing (HPC) systems and discuss its suitability for 1) easy management of applications (e.g., automatic load balancing of MPI application processes), and 2) easy management of the HPC architecture (e.g., automatic migration of OS instances away from physical resources that have been predicted to fail in the near future


High Performance Computing Counter Traffic Live Migration Guest Operating System Page Frame 
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  1. [1]
    The Xen Virtual Machine Monitor, Available: Scholar
  2. [2]
    B. Clark, T. Deshane, E. Dow, S. Evanchik, M. Finlayson, J. Herne, J. N. Matthews, “Xen and the art of repeated research”, USENIX, June 27-July2, 2004.Google Scholar
  3. [3]
    Xen Users’ Manual, Xen v2.0 for x86. Available: Scholar
  4. [4]
    P. Barham, B. Dragovic, K. Fraser, S. Hand, T. Harris, A. Ho, R. Neugebauer, I. Pratt, A. Warfield, “Xen and the art of virtualization”, SOSP 2003Google Scholar
  5. [5]
    C. Clark, K. Fraser, S. Hand, J. Hansen, E. Jul, C. Limpach, I. Pratt, A. Warfield, “ Live migration of virtual machines”, USENIX 2005Google Scholar
  6. [6]
    M. Sottile, V. Chandu, D. Bader, “Performance analysis of parallel programs through message-passing graph”, IPDPS 2006.Google Scholar
  7. [7]
    Intel Cluster Toolkit-2.0. Available: Scholar
  8. [8]
    Open SSI, Single System Image for Linux Clusters. Available: http://www.openssi.orgGoogle Scholar
  9. [9]
    M. Hardt, F. Karlsruhe, “Xen: Experiences and performance measurements”. Linux 2005Google Scholar
  10. [10]
    D. Smith, K. Dukowicz, R. Malone, “Parallel Ocean General Circulation Model”, Physica D, vol. 60, pp. 38-61, 1992MATHCrossRefGoogle Scholar
  11. [11]
    MPICH-A Portable Implementation of MPI. Available: Scholar
  12. [12]
    K. Duda, D. Cheriton, “Borrowed Virtual Time (BVT) scheduling: supporting latency-sensitive threads in a general-purpose scheduler”. In Proc. 17th ACM SIGOPS Symposium on Operating System Principles, 1999, vol 33, no. 5, pp 261-276.Google Scholar
  13. [13]
    Debian-The Universal Operating System. Available: http://www.debian.orgGoogle Scholar
  14. [14]
    R. Minnich, M. Sottile, S. Choi, E. Hendriks, J. McKie, “Right Weight Kernels: an-off-the-shelf alternative to custom Light-Weight Kernels.” Special Issue of the ACM Operating Systems Review Journal 2006.Google Scholar

Copyright information

© Springer 2007

Authors and Affiliations

  • Vaddadi P. Chandu
    • 1
  • Karandeep Singh
    • 2
  1. 1.Georgia Institute of TechnologyAtlanta
  2. 2.University of Maryland, College Park

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