国家自然科学基金面上项目(61872215)
国家自然科学基金重大项目(U1611461)
[1] Wang Z, Li B, Sun L, et al. Cloud-based Social Application Deployment using Local Processing and Global Distribution In: Proceedings of ACM International Conference on Emerging Networking Experiments and Technologies (CoNEXT), 2012. Google Scholar
[2] Wang M, Xu C, Chen X. Differential Privacy Oriented Distributed Online Learning for Mobile Social Video Prefetching. IEEE Trans Multimedia, 2019, 21: 636-651 CrossRef Google Scholar
[3] Kaplan A M, Haenlein M. Users of the world, unite The challenges and opportunities of Social Media. Business Horizons, 2010, 53: 59-68 CrossRef Google Scholar
[4] Cha M, Kwak H, Rodriguez P, et al. I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, 2007. 1--14. Google Scholar
[5] Li H, Wang H, Liu J. Video sharing in online social network: measurement and analysis In: Proceedings of ACM Network and Operating System Support for Digital Audio and Video (NOSSDAV) 2012. Google Scholar
[6] Cheng X, Dale C, Liu J. Statistics and social network of Youtube videos In: Proceedings of IEEE International Workshop on Quality of Service (IWQoS), 2008. Google Scholar
[7] Benevenuto F, Rodrigues T, Almeida V. Video interactions in online video social networks. ACM Trans Multimedia Comput Commun Appl, 2009, 5: 1-25 CrossRef Google Scholar
[8] Lemlouma T, Laya"ıda N. Context-aware adaptation for mobile devices In: Proceedings of IEEE International Conference on Mobile Data Management, 2004. Google Scholar
[9] Li Z, Huang Y, Liu G, et al. Cloud transcoder: bridging the format and resolution gap between internet videos and mobile devices In: Proceedings of ACM Network and Operating System Support for Digital Audio and Video (NOSSDAV), 2012. Google Scholar
[10] Bakshy E, Hofman J, Mason W, et al. Everyone's an influencer: quantifying influence on Twitter In: Proceedings of ACM International Conference on Web Search and Data Mining (WSDM), 2011. Google Scholar
[11] Davidson J, Liebald B, Liu J, et al. The YouTube video recommendation system In: Proceedings of ACM Recommender Systems, 2010. Google Scholar
[12] Debnath S, Ganguly N, Mitra P. Feature weighting in content based recommendation system using social network analysis In: Proceedings of ACM International Conference on World Wide Web (WWW), 2008. Google Scholar
[13] Walter F E, Battiston S, Schweitzer F. A model of a trust-based recommendation system on a social network. Auton Agent Multi-Agent Syst, 2008, 16: 57-74 CrossRef Google Scholar
[14] Isaacman S, Ioannidis S, Chaintreau A, et al. Distributed rating prediction in user generated content streams In: Proceedings of ACM Recommender Systems, 2011. Google Scholar
[15] Saxena M, Sharan U, Fahmy S. Analyzing video services in Web 2.0: a global perspective In: Proceedings of ACM Network and Operating System Support for Digital Audio and Video (NOSSDAV), 2008. Google Scholar
[16] Ager B, Mühlbauer W, Smaragdakis G, et al. Web content cartography In: Proceedings of ACM Internet Measurement Conference (IMC), 2011. Google Scholar
[17] Adhikari V, Jain S, Chen Y, et al. Reverse engineering the YouTube video delivery cloud In: Proceedings of IEEE Hot Topics in Media Delivery Workshop, 2011. Google Scholar
[18] Myers R, Montgomery D, Vining G, et al. Generalized Linear Models Wiley, 2010. Google Scholar
[19] Witten I, Frank E, Hall M. Data Mining: Practical Machine Learning Tools and Techniques San Francisco: Morgan Kaufmann, 2011. Google Scholar
[20] Xu C, Jia S, Zhong L. Socially aware mobile peer-to-peer communications for community multimedia streaming services. IEEE Commun Mag, 2015, 53: 150-156 CrossRef Google Scholar
[21] West R, Zaroo P, Waldspurger C A. Online cache modeling for commodity multicore processors. SIGOPS Oper Syst Rev, 2010, 44: 19-29 CrossRef Google Scholar
[22] Frank B, Poese I, Lin Y. Pushing CDN-ISP collaboration to the limit. SIGCOMM Comput Commun Rev, 2013, 43: 34-44 CrossRef Google Scholar
[23] Carlsson N, Dán G, Eager D, et al. Tradeoffs in cloud and peer-assisted content delivery systems In: Proceedings of IEEE International Conference on Peer-to-Peer Computing (P2P), 2012. Google Scholar
[24] Cervino J, Rodriguez P, Trajkovska I, et al. Testing a cloud provider network for hybrid P2P and cloud streaming architectures In: Proceedings of the 4th IEEE International Conference on Cloud Computing, 2011. Google Scholar
[25] Jin X, Kwok Y, Network aware P2P multimedia streaming: capacity or locality? In: Proceedings of IEEE International Conference on Peer-to-Peer Computing (P2P), 2011. 54--63. Google Scholar
[26] Xu C, Wang M, Chen X. Optimal Information Centric Caching in 5G Device-to-Device Communications. IEEE Trans Mobile Comput, 2018, 17: 2114-2126 CrossRef Google Scholar
[27] Jacobson V, Mosko M, Smetters D, et al. Content-centric networking. Whitepaper, Palo Alto Research Center, 2007. 2--4. Google Scholar
[28] Lederer S, Mueller C, Rainer B, et al. An experimental analysis of dynamic adaptive streaming over http in content centric networks. In: Proceedings of IEEE International Conference on Multimedia and Expo (ICME), 2013. Google Scholar
[29] Zhang X, Wang N, Vassilakis V G. A distributed in-network caching scheme for P2P-like content chunk delivery. Comput Networks, 2015, 91: 577-592 CrossRef Google Scholar
[30] Ma M, Wang Z, Su K, et al. Understanding content placement strategies in smartrouter-based peer video CDN In: Proceedings of ACM SIGMM Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV), 2016. Google Scholar
[31] Davis A, Parikh J, Weihl W. Edgecomputing: extending enterprise applications to the edge of the Internet In: Proceedings of ACM International Conference on World Wide Web (WWW), 2004. Google Scholar
[32] Wang Z, Sun L, Chen X, et al. Propagation-based social-aware replication for social video contents In: Proceedings of ACM International Conference on Multimedia (Multimedia), 2012. Google Scholar
[33] Wu Y, Wu C, Li B, et al. Scaling social media applications into geo-distributed clouds In: Proceedings of IEEE International Conference on Distributed Computing Systems (INFOCOM), 2012. Google Scholar
[34] Xu D, Kulkarni S S, Rosenberg C. Analysis of a CDN-P2P hybrid architecture for cost-effective streaming media distribution. Multimedia Syst, 2006, 11: 383-399 CrossRef Google Scholar
[35] Adhikari V K, Guo Y, Hao F, et al. Unreeling netflix: understanding and improving multi-CDN movie delivery In: Proceedings of IEEE International Conference on Distributed Computing Systems (INFOCOM), 2012. Google Scholar
[36] Kangasharju J, Roberts J, Ross K W. Object replication strategies in content distribution networks. Comput Commun, 2002, 25: 376-383 CrossRef Google Scholar
[37] Cheng X, Liu J. NetTube: exploring social networks for peer-to-peer short video sharing In: Proceedings of IEEE International Conference on Distributed Computing Systems (INFOCOM), 2009. Google Scholar
[38] Benevenuto F, Rodrigues T, Cha M, et al. Characterizing user behavior in online social networks In: Proceedings of ACM Internet Measurement Conference (IMC), 2009. Google Scholar
[39] Li H, Liu J, Xu K, et al. Understanding video propagation in online social networks In: Proceedings of IEEE International Workshop on Quality of Service (IWQoS), 2012. Google Scholar
[40] Li Z, Shen H, Wang H, et al. Socialtube: P2P-assisted video sharing in online social networks. In: Proceedings of IEEE International Conference on Distributed Computing Systems (INFOCOM), 2012. Google Scholar
[41] Nguyen K, Pham C, Tran D, et al. Preserving social locality in data replication for social networks In: Proceedings of IEEE International Conference on Distributed Computing Systems (ICDCS) Workshop on Simplifying Complex Networks for Practitioners, 2011. Google Scholar
[42] Wang Z, Sun L, Yang S, et al. Prefetching strategy in peer-assisted social video streaming. In: Proceedings of ACM International Conference on Multimedia (Multimedia), 2011. Google Scholar
[43] Chen F, Guo K, Lin J, et al. Intra-cloud lightning: building CDNs in the cloud In: Proceedings of IEEE International Conference on Distributed Computing Systems (INFOCOM), 2012. Google Scholar
[44] Fangming Liu , Ye Sun , Bo Li . FS2You: Peer-Assisted Semipersistent Online Hosting at a Large Scale. IEEE Trans Parallel Distrib Syst, 2010, 21: 1442-1457 CrossRef Google Scholar
[45] Furht B, Escalante A. Handbook of Cloud Computing New York: Springer-Verlag, 2010. Google Scholar
[46] Rimal B, Choi E, Lumb I. A taxonomy and survey of cloud computing systems In: Proceedings of IEEE International Joint Conference on INC, IMS and IDC, 2009. Google Scholar
[47] Chohan N, Bunch C, Pang S, et al. AppScale: Scalable and Open AppEngine Application Development and Deployment Cloud Computing, 2010, 34(2): 57--70. Google Scholar
[48] Hofmann P, Woods D. IEEE Internet Comput, 2010, 14: 90-93 CrossRef Google Scholar
[49] Agarwal S, Dunagan J, Jain N, et al. Volley: automated data placement for geo-distributed cloud services. In: Proceedings of the 7th USENIX Symposium on Networked Systems Design and Implementation, 2010. Google Scholar
[50] Li A, Yang X, Kandula S, et al. CloudCmp: comparing public cloud providers In: Proceedings of ACM Internet Measurement Conference (IMC), 2010. Google Scholar
[51] Rehman Z, Hussain F, Hussain O. Towards multi-criteria cloud service selection. In: Proceedings of IEEE International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, 2011. Google Scholar
[52] Huang Y, Li Z, Liu G, et al. Cloud download: using cloud utilities to achieve high-quality content distribution for unpopular videos In: Proceedings of ACM International Conference on Multimedia (Multimedia), 2011. Google Scholar
[53] Miller M. Cloud Computing: Web-Based Applications That Change the Way You Work and Collaborate Online Hoboken: Que Publishing, 2008. Google Scholar
[54] Wang F, Liu J, Chen M. CALMS: cloud-assisted live media streaming for globalized demands with time/region diversities In: Proceedings of IEEE International Conference on Distributed Computing Systems (INFOCOM), 2012. Google Scholar
[55] Wang X, Chen M, Taleb T. Cache in the air: exploiting content caching and delivery techniques for 5G systems. IEEE Commun Mag, 2014, 52: 131-139 CrossRef Google Scholar
[56] Bastug E, Bennis M, Debbah M. Living on the edge: The role of proactive caching in 5G wireless networks. IEEE Commun Mag, 2014, 52: 82-89 CrossRef Google Scholar
[57] Poularakis K, Iosifidis G, Tassiulas L. Approximation Algorithms for Mobile Data Caching in Small Cell Networks. IEEE Trans Commun, 2014, 62: 3665-3677 CrossRef Google Scholar
[58] Khreishah A, Chakareski J, Gharaibeh A. Joint Caching, Routing, and Channel Assignment for Collaborative Small-Cell Cellular Networks. IEEE J Sel Areas Commun, 2016, 34: 2275-2284 CrossRef Google Scholar
[59] Gharaibeh A, Khreishah A, Ji B. A Provably Efficient Online Collaborative Caching Algorithm for Multicell-Coordinated Systems. IEEE Trans Mobile Comput, 2016, 15: 1863-1876 CrossRef Google Scholar
[60] Bharath B N, Nagananda K G, Poor H V. A Learning-Based Approach to Caching in Heterogenous Small Cell Networks. IEEE Trans Commun, 2016, 64: 1674-1686 CrossRef Google Scholar
[61] Li Z, Wilson C, Xu T, et al. Offline downloading in china: a comparative study, In: Proceedings of Proceedings of the 2015 ACM Conference on Internet Measurement Conference, 2015. 473--486. Google Scholar
[62] Chen L, Zhou Y, Jing M, et al. Thunder crystal: a novel crowdsourcing-based content distribution platform. In: Proceedings of the 25th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, 2015. 43--48. Google Scholar
[63] Ma M, Wang Z, Su K, et al. Understanding content placement strategies in smartrouter-based peer video CDN. In: Proceedings of the 26th International Workshop on Network and Operating Systems Support for Digital Audio and Video, 2016. 7. Google Scholar
[64] Gharaibeh A, Khreishah A, Ji B. A Provably Efficient Online Collaborative Caching Algorithm for Multicell-Coordinated Systems. IEEE Trans Mobile Comput, 2016, 15: 1863-1876 CrossRef Google Scholar
[65] Cha M, Mislove A, Gummadi K. A measurement-driven analysis of information propagation in the flickr social network In: Proceedings of ACM International Conference on World Wide Web (WWW), 2009. Google Scholar
[66] Mislove A. Rethinking web content distribution in the social media era In: Proceedings of NSF Workshop on Social Networks and Mobility in the Cloud, 2012. Google Scholar
[67] Wang Z, Liu J, Zhu W. Social Video Content Delivery. Berlin: Springer, 2016. Google Scholar
[68] Ye S, Wu F. Measuring message propagation and social influence on Twitter.com. IJCNDS, 2013, 11: 59-76 CrossRef Google Scholar
[69] Scellato S, Mascolo C, Musolesi M, et al. Distance matters: geo-social metrics for online social networks In: Proceedings of USENIX Conference on Online Social Networks, 2010. Google Scholar
[70] Huffaker B, Fomenkov M, Plummer D, et al. Distance metrics in the Internet In: Proceedings of IEEE International Telecommunications Symposium, 2002. Google Scholar
[71] Yang K S, Shekhar A H, Oliver D. Capacity-Constrained Network-Voronoi Diagram. IEEE Trans Knowl Data Eng, 2015, 27: 2919-2932 CrossRef Google Scholar
[72] Kaashoek M F, Karger D R, Koorde: A simple degree-optimal distributed hash table. In: Proceedings of International Workshop on Peer-to-Peer Systems. Berlin: Springer, 2003. 98--107. Google Scholar
[73] O'reilly T. What is web 2.0. 2005. Google Scholar
[74] Scellato S, Mascolo C, Musolesi M, et al. Track globally, deliver locally: improving content delivery networks by tracking geographic social cascades In: Proceedings of ACM International Conference on Multimedia (Multimedia), 2011. Google Scholar
[75] Brewington B, Cybenko G. How dynamic is the web? Comput Netw, 2000, 33: 257--276. Google Scholar
[76] Kwak H, Lee C, Park H, et al. What is Twitter, a social network or a news media? In: Proceedings of ACM International Conference on World Wide Web (WWW), 2010. Google Scholar
[77] Wang Z, Zhu W, Chen M. CPCDN: Content Delivery Powered by Context and User Intelligence. IEEE Trans Multimedia, 2015, 17: 92-103 CrossRef Google Scholar
[78] Dhillon I. Co-clustering documents and words using bipartite spectral graph partitioning In: Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2001. Google Scholar
[79] Brodersen A, Scellato S, Wattenhofer M. YouTube around the world: geographic popularity of videos In: Proceedings of ACM International Conference on World Wide Web (WWW), 2012. Google Scholar
[80] Wang H, Xu F, Li Y, et al. Understanding mobile traffic patterns of large scale cellular towers in urban environment. In: Proceedings of Proceedings of the 2015 ACM Conference on Internet Measurement Conference, 2015. 225--238. Google Scholar
[81] Ma G, Wang Z, Zhang M. Understanding Performance of Edge Content Caching for Mobile Video Streaming. IEEE J Sel Areas Commun, 2017, 35: 1076-1089 CrossRef Google Scholar
[82] Hu W, Wang Z, Ma M. Edge Video CDN: A Wi-Fi Content Hotspot Solution. J Comput Sci Technol, 2016, 31: 1072-1086 CrossRef Google Scholar
[83] Levi R, Shmoys D B, Swamy C. Lp-based approximation algorithms for capacitated facility location. In: Proceedings of International Conference on Integer Programming and Combinatorial Optimization. Berlin: Springer, 2004. 206--218. Google Scholar
[84] Gavet Y, Pinoli J C. A Geometric Dissimilarity Criterion Between Jordan Spatial Mosaics. Theoretical Aspects and Application to Segmentation Evaluation. J Math Imag Vis, 2012, 42: 25-49 CrossRef Google Scholar
[85] Li Z, Lin J, Akodjenou M I, et al. Watching videos from everywhere: a study of the PPTV mobile VOD system. In: Proceedings of the 2012 ACM Conference on Internet Measurement Conference, 2012. 185--198. Google Scholar
[86] Zhang G P. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 2003, 50: 159-175 CrossRef Google Scholar
[87] Dán G, Carlsson N. Dynamic content allocation for cloud-assisted service of periodic workloads. In: Proceedings of IEEE INFOCOM 2014-IEEE Conference on Computer Communications, 2014. 853--861. Google Scholar
[88] Ma M, Wang Z, Yi K, et al. Joint request balancing and content aggregation in crowdsourced CDN. In: Proceedings of 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), 2017. 1178--1188. Google Scholar
[89] Mills T. Time Series Techniques for Economists Cambridge: Cambridge University Press, 1991. Google Scholar
Figure 1
Research framework
Figure 2
(Color online) Number of regions involved in propagation versus the rank of content
Figure 3
Propagation-based content deployment
Figure 4
(Color online) CPCDN based on data-driven strategies
Figure 5
Importance of content elements in web content distribution
图 6
(网络版彩图) 内容传播的转发数量与用户关注者数量关系
图 7
(网络版彩图) 全局参与的转发数量与本地参与的转发数量关系
Figure 8
(Color online)Voronoi-based region segmentation: (a)$\sim$(c) show the segmentation results, where the original region is divided into sub-region 2, 3 and 4
Figure 9
(Color online) Load prediction for content hotspots
Scalability | Distributed | Processing capability | Stability | Cost | |
Centralized ( | Normal | Weak | Weak | High | High |
Peer-to-Peer ( | Strong | Strong | Weak | Low | Low |
CCN ( | Strong | Normal | Weak | High | High |
Cloud-based ( | Strong | Normal | Strong | High | Low |
Edge-based ( | Strong | Strong | Normal | High | Low |
Beijing | Zhejiang | Guangxi | Shaanxi | |
China Telecom | 366.8 | 281.4 | 338.7 | 249.4 |
China Unicom | 512.2 | – | 462.8 | – |
China Mobile | – | 491.8 | – | – |
From/To | Business | Hospital | Residence | Campus | Attraction | Shoppingmall | Hotel |
Business | 4908 | 2205 | 5114 | 1379 | 595 | 1082 | 657 |
Hospital | 2223 | 1741 | 3479 | 802 | 394 | 698 | 360 |
Residence | 5145 | 3425 | 9994 | 1787 | 995 | 1727 | 907 |
Campus | 1369 | 797 | 1743 | 843 | 230 | 367 | 222 |
Attraction | 596 | 399 | 984 | 215 | 183 | 187 | 123 |
Shoppingmall | 1101 | 692 | 1671 | 358 | 234 | 494 | 169 |
Hotel | 616 | 367 | 928 | 214 | 114 | 202 | 213 |
Notation | Definition |
$\mathcal{A}$ | Set of candidate edge devices |
$\mathcal{A}^*$ | Set of edge devices for content distribution |
$\mathcal{R}$ | Set of regions |
$\mathcal{E}=\{1,2,\ldots,E\}$ | Set of user requests |
$\mathcal{U}$ | Set of users |
$\mathcal{V}$ | Set of content items |
${\boldsymbol~P}$ | User partition |
$W(r)\rightarrow~v$ | Mapping from request $r\in~\mathcal{E}$ to content $v\in~\mathcal{V}$ |
$E_{a}$ | Number of aggregated requests $a~\in~\mathcal{A}^*$ |
$f_{ij}$ | Number of requests redirected from edge device $i$ to $j$ |
$o_{a},~u_{a}$ | Storage and bandwidth capacity of device $a$ |
$f_{a}$ | Upgrade cost for device $a$ |
$d_{ea}$ | Distance between user $e$ and edge device $a$ |
$s_{a}~\in~\{0,1\}$ | Decision variable for device $a$ |
$x_{ea}\in~\{0,1\}$ | Decision variable for device $a$ to serve user $e$ |
$\lambda~\in~[0,1]$ | Optimization weight |
$w_{e}$ | Preference for request $e$ to be redirected |
$\alpha$, $\beta$, $\gamma$ | Control parameters |