OSL: An Optimized Strategy of Data Replicas for Online Social Network

Article Preview

Abstract:

With the continued growth of social network users, nearly all the OSN (Online Social Network) providers replicate users’ data to different server nodes in order to ensure high reliability. Unlike traditional web applications, OSN represents a different class of data system that most of the data is based on friend relationship. In this paper, we propose OSL (Online Social Locality) algorithm, an online optimized strategy of data replicas based on social locality. This algorithm can decide the server node the replicas should be saved, and it improves system performance significantly.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

3230-3233

Citation:

Online since:

December 2012

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Tegeler, F, David Koll, and Xiaoming Fu, "Gemstone: Empowering Decentralized Social Networking with High Data Availability,"GlobalTelecommunications Conference (GLOBECOM 2011), 2011 IEEE, pp.1-6.

DOI: 10.1109/glocom.2011.6134236

Google Scholar

[2] F. Schneider, A.Feldmann, B. Krishnamurthy, and W. Willinger, "Understanding online social network usage from a network perspective,"in Proc. ACM SIGCOMM IMC, 2009, p.35–48.

DOI: 10.1145/1644893.1644899

Google Scholar

[3] Khanh Nguyen, Cuong Pham, Duc A. Tran, and Feng Zhang, "Preserving Social Locality in Datareplication forOnline Social Networks,"ComputerScience, vol. 14, p.129–133, 2011.

Google Scholar

[4] J. M. Pujol, V. Erramilli, G. Siganos, X. Yang, N. Laoutaris, P. Chhabra,and P. Rodriguez, "The little engine(s) that could: scaling online socialnetworks," in Proceedings of the ACM SIGCOMM 2010 Conference.New York, NY, USA: ACM, 2010, p.375–386.

DOI: 10.1145/1851275.1851227

Google Scholar

[5] A. Lakshman and P. Malik, "Cassandra: a decentralized structuredstorage system," SIGOPS Oper. Syst. Rev., vol. 44, p.35–40, April2010.

DOI: 10.1145/1773912.1773922

Google Scholar

[6] G. DeCandia, D. Hastorun, M. Jampani, G. Kakulapati, A. Lakshman,A. Pilchin, S. Sivasubramanian, P. Vosshall, and W. Vogels, "Dynamo:amazon's highly available key-value store," SIGOPS Oper. Syst. Rev.,vol. 41, p.205–220, October 2007.

DOI: 10.1145/1323293.1294281

Google Scholar

[7] F. Chang, J. Dean, S. Ghemawat, W. C. Hsieh, D. A. Wallach, M. Burrows, T. Chandra, A. Fikes, and R. E. Gruber, "Bigtable: a distributedstorage system for structured data," in Proceedings of the 7th USENIXSymposium on Operating Systems Design and Implementation – Volume7, Berkeley, CA, USA, 2006, p.15–15.

DOI: 10.1145/1365815.1365816

Google Scholar

[8] D. Karger, E. Lehman, T. Leighton, R. Panigrahy, M. Levine, andD. Lewin, "Consistent hashing and random trees: distributed cachingprotocols for relieving hot spots on the world wide web," in Proceedingsof the twenty-ninth annual ACM symposium on Theory of computing,ser. STOC '97. New York, NY, USA: ACM, 1997, p.654–663.

DOI: 10.1145/258533.258660

Google Scholar

[9] J. M. Pujol, G. Siganos, V. Erramilli, and P. Rodriguez. Scaling online social networks without pains. NetDB, 2009.

Google Scholar

[10] J. M. Pujol, V. Erramilli, and P. Rodriguez. Divide and conquer: Partitioning online social networks. http://arxiv.org/abs/0905.4918v1, 2009.

Google Scholar