Distributed backup placement in networks

Distributed backup placement in networks We consider the backup placement problem, defined as follows. Some nodes (processors) in a given network have objects (e.g., files, tasks) whose backups should be stored in additional nodes for increased fault resilience. To minimize the disturbance in case of a failure, it is required that a backup copy should be located at a neighbor of the primary node. The goal is to find an assignment of backup copies to nodes which minimizes the maximum load (number or total size of backup copies) over all nodes in the network. It is known that a natural selfish local improvement policy has approximation ratio $$\varOmega (\log n/\log \log n)$$ Ω ( log n / log log n ) ; we show that it may take this policy $$\varOmega (\sqrt{n})$$ Ω ( n ) time to reach equilibrium in the distributed setting. Our main result in this paper is a randomized distributed algorithm which finds a placement in polylogarithmic time and achieves approximation ratio $$O\left(\frac{\log n}{\log \log n}\right)$$ O log n log log n . We obtain this result using a randomized distributed approximation algorithm for f-matching in bipartite graphs that may be of independent interest. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Distributed Computing Springer Journals

Distributed backup placement in networks

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2017 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Computer Communication Networks; Computer Hardware; Computer Systems Organization and Communication Networks; Software Engineering/Programming and Operating Systems; Theory of Computation
ISSN
0178-2770
eISSN
1432-0452
D.O.I.
10.1007/s00446-017-0299-x
Publisher site
See Article on Publisher Site

Abstract

We consider the backup placement problem, defined as follows. Some nodes (processors) in a given network have objects (e.g., files, tasks) whose backups should be stored in additional nodes for increased fault resilience. To minimize the disturbance in case of a failure, it is required that a backup copy should be located at a neighbor of the primary node. The goal is to find an assignment of backup copies to nodes which minimizes the maximum load (number or total size of backup copies) over all nodes in the network. It is known that a natural selfish local improvement policy has approximation ratio $$\varOmega (\log n/\log \log n)$$ Ω ( log n / log log n ) ; we show that it may take this policy $$\varOmega (\sqrt{n})$$ Ω ( n ) time to reach equilibrium in the distributed setting. Our main result in this paper is a randomized distributed algorithm which finds a placement in polylogarithmic time and achieves approximation ratio $$O\left(\frac{\log n}{\log \log n}\right)$$ O log n log log n . We obtain this result using a randomized distributed approximation algorithm for f-matching in bipartite graphs that may be of independent interest.

Journal

Distributed ComputingSpringer Journals

Published: Jun 3, 2017

References

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