Allowing Each Node to Communicate Only Once in a Distributed System: Shared Whiteboard Models
Author
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Becker, Florent
Author
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Kosowski, Adrian
Author
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Nisse, Nicolas
Author
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Rapaport Zimermann, Iván
Author
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Suchan, Karol
Admission date
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2015-08-05T14:52:35Z
Available date
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2015-08-05T14:52:35Z
Publication date
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2015
Cita de ítem
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Distributed Computing, June 2015, Volume 28, Issue 3, pp 189-200
en_US
Identifier
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DOI: 10.1007/s00446-014-0221-8
Identifier
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https://repositorio.uchile.cl/handle/2250/132409
General note
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Artículo de publicación ISI
en_US
Abstract
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In this paper we study distributed algorithms on massive
graphs where links represent a particular relationship between
nodes (for instance, nodes may represent phone numbers
and links may indicate telephone calls). Since such
graphs are massive they need to be processed in a distributed
and streaming way. When computing graph-theoretic properties,
nodes become natural units for distributed computation.
Links do not necessarily represent communication
channels between the computing units and therefore do not
restrict the communication flow. Our goal is to model and
analyze the computational power of such distributed systems
where one computing unit is assigned to each node. Communication
takes place on a whiteboard where each node
is allowed to write at most one message. Every node can
read the contents of the whiteboard and, when activated,
can write one small message based on its local knowledge.
When the protocol terminates its output is computed from
the final contents of the whiteboard. We describe four synchronization
models for accessing the whiteboard. We show
that message size and synchronization power constitute two
orthogonal hierarchies for these systems. We exhibit problems
that separate these models, i.e., that can be solved in
one model but not in a weaker one, even with increased
message size. These problems are related to maximal independent
set and connectivity. We also exhibit problems that
require a given message size independently of the synchronization
model.