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Improving cloud-based online social network data placement and replication

Khalajzadeh, Hourieh, Yuang, Dong, Grundy, John and Yang, Yun 2016, Improving cloud-based online social network data placement and replication, in IEEE CLOUD 2016: Proceedings of the 9th IEEE International Conference on Cloud Computing, IEEE, Piscataway, NJ, pp. 1-8.

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Title Improving cloud-based online social network data placement and replication
Author(s) Khalajzadeh, Hourieh
Yuang, Dong
Grundy, JohnORCID iD for Grundy, John orcid.org/0000-0003-4928-7076
Yang, Yun
Conference name Cloud Computing. Conference (9th : 2016 : San Francisco, Calif.)
Conference location San Francisco, Calif.
Conference dates 27 Jun - 2 jul. 2016
Title of proceedings IEEE CLOUD 2016: Proceedings of the 9th IEEE International Conference on Cloud Computing
Publication date 2016
Start page 1
End page 8
Total pages 8
Publisher IEEE
Place of publication Piscataway, NJ
Keyword(s) Online social network
data placement
data replication
latency
genetic algorithm
Summary Online social networks make it easier for people to find and communicate with other people based on shared interests, values, membership in particular groups, etc. Common social networks such as Facebook and Twitter have hundreds of millions or even billions of users scattered all around the world sharing interconnected data. Users demand low latency access to not only their own data but also theirfriends’ data, often very large, e.g. videos, pictures etc. However, social network service providers have a limited monetary capital to store every piece of data everywhere to minimise users’ data access latency. Geo-distributed cloud services with virtually unlimited capabilities are suitable for large scale social networks data storage in different geographical locations. Key problems including how to optimally store and replicate these huge datasets and how to distribute the requests to different datacenters are addressed in this paper. A novel genetic algorithm-based approach is used to find a near-optimal number of replicas for every user’s data and a near-optimal placement of replicas to minimise monetary cost while satisfying latency requirements for all users. Experiments on a large Facebook dataset demonstrate our technique’s effectiveness in outperforming other representative placement and replication strategies.
Language eng
Field of Research 080309 Software Engineering
Socio Economic Objective 890202 Application Tools and System Utilities
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Grant ID LP130100324
Copyright notice ©2016, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30085955

Document type: Conference Paper
Collection: School of Information Technology
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