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https://doi.org/10.1109/ccgrid...
Article . 2013 . Peer-reviewed
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Heterogeneity: The Key to Achieve Power-Proportional Computing

Authors: Georges Da Costa;

Heterogeneity: The Key to Achieve Power-Proportional Computing

Abstract

The Smart 2020 report on low carbon economy in the information age shows that 2% of the global CO2 footprint will come from ICT in 2020. Out of these, 18% will be caused by data-centers, while 45% will come from personal computers. Classical research to reduce this footprint usually focuses on new consolidation techniques for global data-centers. In reality, personal computers and private computing infrastructures are here to stay. They are subject to irregular workload, and are usually largely under-loaded. Most of these computers waste tremendous amount of energy as nearly half of their maximum power consumption comes from simply being switched on. The ideal situation would be to use proportional computers that use nearly 0W when lightly loaded. This article shows the gains of using a perfectly proportional hardware on different type of data-centers: 50% gains for the servers used during 98 World Cup, 20% to the already optimized Google servers. Gains would attain up to 80% for personal computers. As such perfect hardware still does not exist, a real platform composed of Intel I7, Intel Atom and Raspberry Pi is evaluated. Using this infrastructure, gains are of 20% for the World Cup data-center, 5% for Google data-centers and up to 60% for personal computers.

Country
France
Keywords

Energy efficiency, Power proportional, Architectures Matérielles, Heterogeneous architectures, Système d'exploitation, Réseaux et télécommunications, Large scale, Systèmes embarqués, Data-centers

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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!
views
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