Improving cloud-based online social network data placement and replication

Hourieh Khalajzadeh, Dong Yuan, John Grundy, Yun Yang

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

23 Citations (Scopus)


Online social networks make it more convenient for people to find and communicate with other people 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, 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 Facebook dataset demonstrate our technique's effectiveness in outperforming other representative placement and replication strategies.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE 9th International Conference on Cloud Computing, CLOUD 2016
EditorsIan Foster , Nimish Radia
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Print)9781509026197
Publication statusPublished - 17 Jan 2017
Externally publishedYes
EventIEEE International Conference on Cloud Computing 2016 - San Francisco, United States of America
Duration: 27 Jun 20162 Jul 2016
Conference number: 9th (Proceedings)


ConferenceIEEE International Conference on Cloud Computing 2016
Abbreviated titleCLOUD 2016
Country/TerritoryUnited States of America
CitySan Francisco
Internet address


  • Data placement
  • Data replication
  • Genetic algorithm
  • Latency
  • Online social network

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