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Improving cloud-based online social network data placement and replication

Version 2 2024-06-04, 13:05
Version 1 2016-09-06, 19:33
conference contribution
posted on 2024-06-04, 13:05 authored by Hourieh KhalajzadehHourieh Khalajzadeh, D Yuang, JC Grundy, Y Yang
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 their friends’ 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.





San Francisco, Calif.

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Publication classification

E Conference publication, E1 Full written paper - refereed

Copyright notice

2016, IEEE

Title of proceedings

IEEE CLOUD 2016: Proceedings of the 9th IEEE International Conference on Cloud Computing


Cloud Computing. Conference (9th : 2016 : San Francisco, Calif.)



Place of publication

Piscataway, NJ