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Scalable fault tolerance

Authors: Shay Kutten;

Scalable fault tolerance

Abstract

As communication networks grow, existing fault handling tools become increasingly unaffordable. In many cases the reason is that they involve global measures such as global time-outs or reset procedures, and their cost grows with the size of the network. Rather, for a fault handling mechanism to scale to large networks, it should involve local measures, or, at worse, fault local measures, i.e. measures the cost of which depends only on the number of failed nodes (which, thanks to to-day's technology, grows much slower than the networks). This decreases the recovery time and, moreover, often allows the non-faulty regions of the networks to continue their operation even during the recovery of the faulty parts. We describe several research ideas that lead in this direction.

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    popularity
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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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!
0
Average
Average
Average