Journal of Computer Science and Technology ›› 2019, Vol. 34 ›› Issue (1): 234-252.doi: 10.1007/s11390-019-1907-y

Special Issue: Artificial Intelligence and Pattern Recognition; Data Management and Data Mining

• Regular Paper • Previous Articles    

A Cost-Efficient Approach to Storing Users' Data for Online Social Networks

Jing-Ya Zhou1,2,3, Member, CCF, ACM, Jian-Xi Fan1, Member, CCF, Cheng-Kuan Lin1, Member, CCF, and Bao-Lei Cheng1, Member, CCF   

  1. 1 School of Computer Science and Technology, Soochow University, Suzhou 215006, China;
    2 Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou 215006, China;
    3 Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing University Nanjing 210023, China
  • Received:2017-10-30 Revised:2018-10-11 Online:2019-01-05 Published:2019-01-12
  • About author:Jing-Ya Zhou received his B.S. degree in computer science from Anhui Normal University, Wuhu, in 2005, and his Ph.D. degree in computer science from Southeast University, Nanjing, in 2013. He is currently an assistant professor with the School of Computer Science and Technology, Soochow University, Suzhou. His research interests include cloud and edge computing, parallel and distributed systems, online social networks and data center networking.
  • Supported by:
    The work was supported by the National Natural Science Foundation of China under Grant Nos. 61502328, 61672370, and 61572337, the Jiangsu Planned Projects for Postdoctoral Research Funds under Grant No. 1701173B, the Open Project Program of Jiangsu Provincial Key Laboratory for Computer Information Processing Technology under Grant No. KJS1740, and the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant No. 18KJA520009.

As users increasingly befriend others and interact online via their social media accounts, online social networks (OSNs) are expanding rapidly. Confronted with the big data generated by users, it is imperative that data storage be distributed, scalable, and cost-efficient. Yet one of the most significant challenges about this topic is determining how to minimize the cost without deteriorating system performance. Although many storage systems use the distributed key value store, it cannot be directly applied to OSN storage systems. And because users' data are highly correlated, hash storage leads to frequent inter-server communications, and the high inter-server traffic costs decrease the OSN storage system's scalability. Previous studies proposed conducting network partitioning and data replication based on social graphs. However, data replication increases storage costs and impacts traffic costs. Here, we consider how to minimize costs from the perspective of data storage, by combining partitioning and replication. Our cost-efficient data storage approach supports scalable OSN storage systems. The proposed approach co-locates frequently interactive users together by conducting partitioning and replication simultaneously while meeting load-balancing constraints. Extensive experiments are undertaken on two realworld traces, and the results show that our approach achieves lower cost compared with state-of-the-art approaches. Thus we conclude that our approach enables economic and scalable OSN data storage.

Key words: online social network; inter-server traffic cost; storage cost; network partitioning; data replication;

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