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Accelerating Application Migration in HPC

  • Ramy Gad
  • Simon Pickartz
  • Tim Süß
  • Lars Nagel
  • Stefan Lankes
  • André Brinkmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9945)

Abstract

It is predicted that the number of cores per node will rapidly increase with the upcoming era of exascale supercomputers. As a result, multiple applications will have to share one node and compete for the (often scarce) resources available on this node. Furthermore, the growing number of hardware components causes a decrease in the mean time between failures. Application migration between nodes has been proposed as a tool to mitigate these two problems: Bottlenecks due to resource sharing can be addressed by load balancing schemes which migrate applications; and hardware errors can often be tolerated by the system if faulty nodes are detected and processes are migrated ahead of time.

Virtual Machine (VM) migration currently seems to be the most promising technique for such approaches as it provides a strong level of isolation. However, the migration time of virtual machines is higher than the respective migration time on the process level. This can be explained by the additional virtualization layer in the memory hierarchy.

In this paper, we propose a technique for the acceleration of VM migration. We take advantage of the fact that freed memory regions within the guest system are not recognized by the hypervisor. Therefore, we fill them with zeros such that zero-page detection and compression can work more efficiently. We demonstrate that the approach reduces migration time by up to 19 % with a negligible overhead for some applications.

Keywords

Virtual Machine Migration Time Target Node Virtual Machine Migration Runtime Overhead 
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.

Notes

Acknowledgment and Availability

This work was supported by the German Ministry for Education and Research (BMBF) under project grant 01|H13004 (FAST).

The zeroing preload library is publicly available under https://version.zdv.Uni-Mainz.DE/anonscm/git/memory-zeroing/memory-zeroing.git.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Zentrum für DatenverarbeitungJohannes Gutenberg-Universität MainzMainzGermany
  2. 2.Institute for Automation of Complex Power Systems, E.ON Energy Research CenterRWTH AachenAachenGermany

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