Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/140032
Title: Traffic-optimized data placement for social media
Authors: Tang, Jing
Tang, Xueyan
Yuan, Junsong
Keywords: Engineering::Computer science and engineering
Issue Date: 2017
Source: Tang, J., Tang, X., & Yuan, J. (2018). Traffic-optimized data placement for social media. IEEE Transactions on Multimedia, 20(4), 1008-1023. doi:10.1109/TMM.2017.2760627
Journal: IEEE Transactions on Multimedia
Abstract: Social media users are generating data on an unprecedented scale. Distributed storage systems are often used to cope with explosive data growth. Data partitioning and replication are two interrelated data placement issues affecting the interserver traffic caused by user-initiated read and write operations in distributed storage systems. This paper investigates how to minimize the interserver traffic among a cluster of social media servers through joint data partitioning and replication optimization. We formally define the problem and study its hardness. We then propose a traffic-optimized partitioning and replication (TOPR) method to continuously adapt data placement according to various dynamics. Evaluations with real Twitter and LiveJournal social graphs show that TOPR not only reduces the interserver traffic significantly but also saves much storage cost of replication compared to state-of-the-art methods. We also benchmark TOPR against the offline optimum by a binary linear program.
URI: https://hdl.handle.net/10356/140032
ISSN: 1520-9210
DOI: 10.1109/TMM.2017.2760627
Rights: © 2017 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:IGS Journal Articles

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