Paper Info
Title
Cost-Effective Social Network Data Placement and Replication Using Graph-Partitioning
Abstract
Social network users are connected based on shared interests, ideas, association with different 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 their friends' data. However, social network service providers wish to pay as less as possible to store all data items to meet users' data access latency requirement. 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 data items and how to distribute the requests to different datacentres are addressed in this paper. A novel graph-partitioning based approach is proposed to find a near-optimal data placement of replicas to minimise monetary cost while satisfying the latency requirement. Experiments on a Facebook dataset demonstrate our technique's effectiveness in outperforming other representative placement and replication strategies.
Year
DOI
Venue
2017
10.1109/IEEE.ICCC.2017.16
2017 IEEE International Conference on Cognitive Computing (ICCC)
Keywords
DocType
ISBN
social network,data placement,data replication,latency,storage cost,graph-partitioning
Conference
978-1-5386-2009-0
Citations 
PageRank 
References 
0
0.34
16
Authors
4
Name
Order
Citations
PageRank
Hourieh Khalajzadeh1136.05
Dong Yuan276848.06
John C. Grundy32401233.83
Yun Yang42103150.49