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Dealing with Skewed Data in Structured Overlays using Variable Hash Functions

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Abstract

Storing highly skewed data in a distributed system has become a very frequent issue, in particular with the emergence of semantic web and Big Data. This often leads to biased data dissemination among nodes. Addressing load imbalance is necessary, especially to minimize response time and avoid workload being handled by only one or few nodes. Our contribution aims at dynamically managing load imbalance by allowing multiple hash functions on different peers, while maintaining consistency of the overlay. Our experiments, on highly skewed data sets from the semantic web, show we can distribute data on at least 300 times more peers than when not using any load balancing strategy.
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Dates and versions

hal-01101678 , version 1 (09-01-2015)

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Maeva Antoine, Fabrice Huet. Dealing with Skewed Data in Structured Overlays using Variable Hash Functions. The 15th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), The University of Hong Kong, Dec 2014, Hong Kong, Hong Kong SAR China. pp.42-48, ⟨10.1109/PDCAT.2014.15⟩. ⟨hal-01101678⟩
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