NDLI: Gossip-based partitioning and replication for Online Social Networks

Content Provider	IEEE Xplore Digital Library
Author	Nasir, M.A.U. Rahimian, F. Girdzijauskas, S.
Copyright Year	2014
Description	Author affiliation: KTH R. Inst. of Technol., Stockholm, Sweden (Nasir, M.A.U.; Girdzijauskas, S.) \|\| Swedish Inst. of Comput. Sci. (SICS), Kista, Sweden (Rahimian, F.)
Abstract	Online Social Networks (OSNs) have been gaining tremendous growth and popularity in the last decade, as they have been attracting billions of users from all over the world. Such networks generate petabytes of data from the social interactions among their users and create many management and scalability challenges. OSN users share common interests and exhibit strong community structures, which create complex dependability patterns within OSN data, thus, make it difficult to partition and distribute in a data center environment. Existing solutions, such as, distributed databases, key-value stores and auto scaling services use random partitioning to distribute the data across a cluster, which breaks existing dependencies of the OSN data and may generate huge inter-server traffic. Therefore, there is a need for intelligent data allocation strategy that can reduce the network cost for various OSN operations. In this paper, we present a gossip-based partitioning and replication scheme that efficiently splits OSN data and distributes the data across a cluster. We achieve fault tolerance and data locality, for one-hop neighbors, through replication. Our main contribution is a social graph placement strategy that divides the social graph into predefined size partitions and periodically updates the partitions to place socially connected users together. To evaluate our algorithm, we compare it with random partitioning and a state-of-the-art solution SPAR. Results show that our algorithm generates up to four times less replication overhead compared to random partitioning and half the replication overhead compared to SPAR.
Starting Page	33
Ending Page	42
File Size	328863
Page Count	10
File Format	PDF
ISBN	9781479958771
DOI	10.1109/ASONAM.2014.6921557
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis replication Simulated annealing online social networks partitioning Peer-to-peer computing Partitioning algorithms Servers Facebook scalability
Content Type	Text
Resource Type	Article

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