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Journal of Computer Science and Technology

, Volume 34, Issue 1, pp 234–252 | Cite as

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

  • Jing-Ya ZhouEmail author
  • Jian-Xi Fan
  • Cheng-Kuan Lin
  • Bao-Lei Cheng
Regular Paper
  • 9 Downloads

Abstract

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.

Keywords

online social network inter-server traffic cost storage cost network partitioning data replication 

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Notes

Acknowledgement(s)

We thank the anonymous reviewers and editors for their valuable suggestions that help to improve the presentation of the paper.

Supplementary material

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References

  1. [1]
    Althoff T, Jindal P, Leskovec J. Online actions with offline impact: How online social networks influence online and offline user behavior. In Proc. the 10th ACM International Conference on Web Search and Data Mining, February 2017, pp.537-546.Google Scholar
  2. [2]
    Peng S C, Yang A M, Cao L H, Yu S, Xie D Q. Social influence modeling using information theory in mobile social networks. Information Sciences, 2017, 379: 146-159.CrossRefGoogle Scholar
  3. [3]
    Wang F,Wang H Y, Xu K,Wu J H, Jia X H. Characterizing information diffusion in online social networks with linear diffusive model. In Proc. the 33rd International Conference on Distributed Computing Systems, July 2013, pp.307-316.Google Scholar
  4. [4]
    Al-FaresM, Loukissas A, Vahdat, A. A scalable, commodity data center network architecture. In Proc. the ACM SIGCOMM Conference on Data communication, August 2008, pp.63-74.Google Scholar
  5. [5]
    Shvachko K, Kuang H, Radia S, Chansler R. The Hadoop distributed file system. In Proc. the 26th IEEE Symposium on Mass Storage Systems and Technologies, May 2010.Google Scholar
  6. [6]
    Lakshman A, Malik P. Cassandra: A decentralized structured storage system. ACM SIGOPS Operating Systems Review, 2010, 44(2): 35-40.CrossRefGoogle Scholar
  7. [7]
    Sumbaly R, Kreps J, Gao L, Feinberg A, Soman C, Shah S. Serving large-scale batch computed data with project Voldemort. In Proc. the 10th USENIX Conference on File and Storage Technologies, February 2012, pp.223-235.Google Scholar
  8. [8]
    Karypis G, Kumar V. A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM Journal on Scientific Computing, 1998, 20(1): 359-392.MathSciNetCrossRefzbMATHGoogle Scholar
  9. [9]
    Chen H H, Jin H, Wu S. Minimizing inter-server communications by exploiting self-similarity in online social networks. IEEE Transactions on Parallel and Distributed Systems, 2016, 27(4): 1116-1130.CrossRefGoogle Scholar
  10. [10]
    Liu G X, Shen H Y, Chandler H. Selective data replication for online social networks with distributed datacenters. IEEE Transactions on Parallel and Distributed Systems, 2016, 27(8): 2377-2393.CrossRefGoogle Scholar
  11. [11]
    Pujol J M, Erramilli V, Siganos G, Yang X, Laoutaris N, Chhabra P, Rodriguez P. The little engine(s) that could: Scaling online social networks. IEEE/ACM Transactions on Networking, 2012, 20(4): 1162-1175.CrossRefGoogle Scholar
  12. [12]
    Tran D A, Zhang T. S-PUT: An EA-based framework for socially aware data partitioning. Computer Networks, 2014, 75: 504-518.CrossRefGoogle Scholar
  13. [13]
    Yu B Y, Pan J P. Location-aware associated data placement for geo-distributed data intensive applications. In Proc. the 34th IEEE International Conference on Computer Communications, April 2015, pp.603-611.Google Scholar
  14. [14]
    Zhou J Y, Fan J X, Cheng B L, Jia J C. Optimizing interserver communications by exploiting overlapping communities in online social networks. In Proc. the 16th International Conference on Algorithms and Architectures for Parallel Processing, December 2016, pp.231-244.Google Scholar
  15. [15]
    Tran D A, Nguyen K, Pham C. S-CLONE: Socially-aware data replication for social networks. Computer Networks, 2012, 56(7): 2001-2013CrossRefGoogle Scholar
  16. [16]
    Zhang J H, Chen J, Luo J Z, Song A B. Efficient locationaware data placement for data-intensive applications in geodistributed scientific data centers. Tsinghua Science and Technology, 2016, 21(5): 471-481.CrossRefGoogle Scholar
  17. [17]
    Jiao L, Lit J, Du W, Fu X M. Multi-objective data placement for multi-cloud socially aware services. In Proc. the 33rd IEEE International Conference on Computer Communications, April 2014, pp.28-36.Google Scholar
  18. [18]
    Gregory S. Finding overlapping communities in networks by label propagation. New Journal of Physics, 2010, 12(10): Article No. 103018.Google Scholar
  19. [19]
    Lancichinetti A, Fortunato S, Kertész J. Detecting the overlapping and hierarchical community structure in complex networks. New Journal of Physics, 2009, 11(3): Article No. 033015.Google Scholar
  20. [20]
    Qiao S, Han N, Zhang K, Zou L, Wang H, Alberto G L. Algorithm for detecting overlapping communities from complex network big data. Journal of Software, 2017, 28(3): 631-647. (in Chinese)MathSciNetzbMATHGoogle Scholar
  21. [21]
    Wilson C, Sala A, Puttaswamy K P N, Zhao B Y. Beyond social graphs: User interactions in online social networks and their implications. ACM Transactions on the Web, 2012, 6(4): Article No. 17.Google Scholar
  22. [22]
    Gjoka M, Kurant M, Butts C T, Markopoulou A. Walking in Facebook: A case study of unbiased sampling of OSNs. In Proc. the 29th IEEE International Conference on Computer Communications, March 2010, pp.2498-2506.Google Scholar
  23. [23]
    Jiang J, Wilson C, Wang X, Sha W P, Huang P, Dai Y F, Zhao B Y. Understanding latent interactions in online social networks. ACM Transactions on the Web, 2013, 7(4): Article No. 18.Google Scholar
  24. [24]
    Benevenuto F, Rodrigues T, Cha M, Almeida V A F. Characterizing user behavior in online social networks. In Proc. the 9th ACM SIGCOMM Conference on Internet Measurement, November 2009, pp.49-62.Google Scholar
  25. [25]
    Roy A, Zeng H, Bagga J, Porter G, Snoeren A C. Inside the social network’s (datacenter) network. In Proc. the 2015 ACM Conference on Special Interest Group on Data Communication, August 2015, pp.123-137.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Jing-Ya Zhou
    • 1
    • 2
    • 3
    Email author
  • Jian-Xi Fan
    • 1
  • Cheng-Kuan Lin
    • 1
  • Bao-Lei Cheng
    • 1
  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.Provincial Key Laboratory for Computer Information Processing TechnologySoochow UniversitySuzhouChina
  3. 3.Collaborative Innovation Center of Novel Software Technology and IndustrializationNanjing UniversityNanjingChina

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