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Migration Techniques in HPC Environments

  • Simon Pickartz
  • Ramy Gad
  • Stefan Lankes
  • Lars Nagel
  • Tim Süß
  • André Brinkmann
  • Stephan Krempel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8806)

Abstract

Process migration is an important feature in modern computing centers as it allows for a more efficient use and maintenance of hardware. Especially in virtualized infrastructures it is successfully exploited by schemes for load balancing and energy efficiency. One can divide the tools and techniques into three groups: Process-level migration, virtual machine migration, and container-based migration.

This paper presents a qualitative and quantitative investigation of the different migration types for their application in (HPC). In addition to an overhead analysis of the various migration frameworks, our performance indicators include the migration time. The overall analysis suggests that VM migration has the most advantages and can even compete performance-wise.

The results are applied in the research project FaST addressing the problem of process scheduling in exascale environments. It is assumed that a shift in hardware architectures will result in a growing gap between the performance of CPUs and that of other resources like I/O. To avoid that these resources become bottlenecks, we suggest to monitor key performance indicators and, if conducive, trigger local amendments to the schedule requiring the efficient migration of jobs so that the downtime is reduced to a minimum.

Keywords

Virtual Machine Migration Time Virtual Machine Migration Live Migration Virtualization Layer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Simon Pickartz
    • 1
  • Ramy Gad
    • 2
  • Stefan Lankes
    • 1
  • Lars Nagel
    • 2
  • Tim Süß
    • 2
  • André Brinkmann
    • 2
  • Stephan Krempel
    • 3
  1. 1.Institute for Automation of Complex Power Systems, E.ON Energy Research CenterRWTH Aachen UniversityAachenGermany
  2. 2.Zentrum für DatenverarbeitungJohannes Gutenberg UniversitätMainzGermany
  3. 3.ParTec Cluster Competence Center GmbHMunichGermany

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