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Concurrency and Computation Practice and Experience
Article . 2018 . Peer-reviewed
License: CC BY
Data sources: Crossref
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DBLP
Article . 2018
Data sources: DBLP
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Prospects and challenges of virtual machine migration in HPC

Authors: Simon Pickartz; Carsten Clauss; Jens Breitbart; Stefan Lankes; Antonello Monti;

Prospects and challenges of virtual machine migration in HPC

Abstract

SummaryThe continuous growth of supercomputers is accompanied by increased complexity of the intra‐node level and the interconnection topology. Consequently, the whole software stack ranging from the system software to the applications has to evolve, eg, by means of fault tolerance and support for the rising intra‐node parallelism. Migration techniques are one means to address these challenges. On the one hand, they facilitate the maintenance process by enabling the evacuation of individual nodes during runtime, ie, the implementation of fault avoidance. On the other hand, they enable dynamic load balancing for an improvement of the system's efficiency. However, these prospects come along with certain challenges. On the process level, migration mechanisms have to resolve so‐called residual dependencies to the source node, eg, the communication hardware. On the job level, migrations affect the communication topology, which should be addressed by the communication stack, ie, the optimal communication path between a pair of processes might change after a migration. In this article, we explore migration mechanisms for HPC and discuss their prospects as well as the challenges. Furthermore, we present solutions enabling their efficient usage in this domain. Finally, we evaluate our prototype co‐scheduler leveraging migration for workload optimization.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Average
Average
Top 10%
hybrid