@article{ref1,
title="Traffic-optimized data placement for social media",
journal="IEEE transactions on multimedia",
year="2017",
author="Tang, Jing and Tang, Xueyan and Yuan, Junsong",
volume="PP",
number="99",
pages="e8060548-e8060548",
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 inter-related data placement issues affecting the inter-server traffic caused by user-initiated read and write operations in distributed storage systems. This paper investigates how to minimize the inter-server 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 inter-server 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.   Keywords: Twitter-Traffic-Status<p /> <p>Language: en</p>",
language="en",
issn="1520-9210",
doi="10.1109/TMM.2017.2760627",
url="http://dx.doi.org/10.1109/TMM.2017.2760627"
}