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Affinity replica selection in distributed systems

Awang, W. S. W., Deris, M. M., Rana, Omer F., Zarina, M. and Rose, A. N. M. 2019. Affinity replica selection in distributed systems. Presented at: PaCT 2019: International Conference on Parallel Computing Technologies, Almaty, Kazakhstan, 19-23 August 2019. Published in: Malyshkin, V. ed. Parallel Computing Technologies: 15th International Conference, PaCT 2019, Almaty, Kazakhstan, August 19–23, 2019, Proceedings. Springer, pp. 385-399. 10.1007/978-3-030-25636-4_30

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Abstract

Replication is one of the key techniques used in distributed systems to improve high data availability, data access performance and data reliability. To optimize the maximum benefits from file replication, a systems that includes replicas need a strategy for selecting and accessing suitable replicas. A replica selection strategy determines the available replicas and chooses the most access files. In most of these access frequency based solutions or popularity of files are assuming that files are independent of each other. In contrast, distributed systems such as peer-to-peer file sharing, and mobile database, files may be dependent or correlated to one another. Thus, this paper focused on the combination of popularity and affinity files as the most important parameters in selecting replicas in distributed environments. Herein, a replica selection is proposed focusing on popular files and affinity files. The idea is to improve data availability in distributed data replica selection strategy. A P2P simulator, PeerSim, is used to evaluate the performance of the dynamic replica selection strategy. The simulation results provided a proof that the proposed affinity replica selection has contributed towards a new dimension of replica selection strategy that incorporates the affinity and popularity of file replicas in distributed systems.

Item Type: Conference or Workshop Item (Paper)
Date Type: Published Online
Status: Published
Schools: Computer Science & Informatics
Publisher: Springer
ISBN: 9783030256357
Funders: UNISZA (UniSZA/2017/DPU/72)
Last Modified: 14 Aug 2019 15:34
URI: http://orca-mwe.cf.ac.uk/id/eprint/124917

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