Premium
Towards traffic minimization for data placement in online social networks
Author(s) -
Zhou Jingya,
Fan Jianxi,
Wang Jin,
Cheng Baolei,
Jia Juncheng
Publication year - 2016
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.3869
Subject(s) - computer science , server , locality , network topology , data center , social graph , hash function , distributed computing , partition (number theory) , graph partition , pairwise comparison , graph , theoretical computer science , computer network , computer security , social media , world wide web , artificial intelligence , mathematics , philosophy , linguistics , combinatorics
Summary With the increasing number of users and a huge scale of data, the service providers of Online Social Networks (OSNs) are facing the problem of how to place users' data to multiple servers. Key‐value stores solve the problem based on consistent hashing, and have become a defacto standard. However, random placement manner of hashing cannot preserve social locality, which leads to high intra‐data center traffic and unpredictable response time. Many existing works solve the problem by using graph partitioning algorithms. These works have two drawbacks: First, the social graph is constructed with ordinary pairwise graph that cannot fully reflect multi‐participant interactions often occurring in OSNs. Second, the underlying network topologies of data center have never been considered. This paper investigates the problem of traffic minimization for OSNs data storage. Motivated by maximally preserving both social locality and distance locality, we formulate the problem as two sub‐problems — hypergraph partitioning and partition‐to‐server mapping, and propose a two‐phase data placement (TDP) scheme. Specifically we present two algorithms to solve partition‐to‐server mapping over two widely used network topologies ( i.e., tree and BCube). Evaluations with a large scale Facebook trace show that TDP significantly reduces intra‐data center traffic as well as load balancing across servers. Copyright © 2016 John Wiley & Sons, Ltd.