Data placement cost optimization and load balancing for online social networks

Yan Yang, Xuejun Li, Hourieh Khalajzadeh, Xiao Liu, Xia Ji, Fulan Qian

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

1 Citation (Scopus)


With the rapid development of broadband wireless technology and popularity of the intelligence devices, the number of online social networks (OSNs) users is growing every day. The huge data of users need to be replicated and placed over multiple geographically distributed clouds to be accessible by the users in a reasonable time. Therefore, reducing the storage cost and keeping a reasonable performance of the storage system becomes more and more important. Storing data items in the same cloud may minimize cost but incurs the worst imbalance. In addition, the data transfer cost in OSNs with millions of connections is significant and is not negligible. Therefore, our goal is to optimize the total cost of data storage and transfer while guaranteeing users' latency requirements and keeping a reasonable load balancing. A novel graph-partitioning based algorithm is proposed to achieve our goal. Experiments on two different Facebook datasets demonstrate that our strategy can significantly reduce the total cost and keep a reasonable load balancing in comparison with other representative placement strategies.

Original languageEnglish
Title of host publicationProceedings - 2019 7th International Conference on Advanced Cloud and Big Data, CBD 2019
EditorsXiaohua Jia, Qiaoming Zhu, Fang Dong
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781728151403, 9781728151410
ISBN (Print)9781728151427
Publication statusPublished - 2019
EventInternational Conference on Advanced Cloud and Big Data 2019 - Suzhou, China
Duration: 21 Sep 201922 Sep 2019
Conference number: 7th


ConferenceInternational Conference on Advanced Cloud and Big Data 2019
Abbreviated titleCBD 2019
Internet address


  • Cost
  • Data placement
  • Graph-partitioning
  • Latency
  • Load balancing
  • Social networks

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