Joint Content Replication and Request Routing for Social Video Distribution Over Cloud CDN: A Community Clustering Method

被引:91
作者
Hu, Han [1 ]
Wen, Yonggang [1 ]
Chua, Tat-Seng [2 ]
Huang, Jian [3 ]
Zhu, Wenwu [4 ]
Li, Xuelong [5 ]
机构
[1] Nanyang Technol Univ, Sch Comp Engn, Singapore 639798, Singapore
[2] Natl Univ Singapore, Sch Comp, Singapore 117417, Singapore
[3] Sun Yat Sen Univ, Guangzhou 510275, Guangdong, Peoples R China
[4] Tsinghua Univ, Beijing 100084, Peoples R China
[5] Chinese Acad Sci, Ctr OPT IMagery Anal & Learning, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China
关键词
Cloud content delivery network (CDN); community detection; social video; TV;
D O I
10.1109/TCSVT.2015.2455712
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The increasing popularity of online social networks (OSNs) has been transforming the dissemination pattern of social video contents. We can utilize the social information propagation pattern to improve the efficiency of social video distribution. In this paper, motivated by the social community classification, we present a social video replication and user request dispatching mechanism in the cloud content delivery network architecture to reduce the system operational cost, while guaranteeing the averaged service latency. Specifically, we first present a community classification method that clusters social users with social relationships, close geolocations, and similar video watching interests into various communities. Then, we conduct a large-scale measurement on a real OSN system to study the diversities of social video propagation and the effectiveness of our communities on smoothing the diversity. Finally, we propose the community-based video replication and request dispatching strategy and formulate it as a constrained optimization problem. Based on a stochastic optimization framework, we derive an online solution and rigorously prove the optimality. We evaluate our algorithm on a real trace under realistic settings and demonstrate that our algorithm can reduce the monetary cost by 30% against traditional approaches with the same service latency.
引用
收藏
页码:1320 / 1333
页数:14
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