Skip to main content
SpringerLink
Log in

A data-driven and the deep learning based CDN recommendation framework for ICPs

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

It is a significant trend that the Internet Content Providers (ICPs) improve the quality of service and reduce the cost of content distribution by the Content Delivery Networks (CDNs). In order to spend the less money to get better services, ICPs need to find a lot of information about CDNs, such as server deployment, performance, price and so on, to determine whether CDN services satisfy their requirements. Unfortunately, these information can’t be obtained by third party due to business secret. ICPs still choose CDNs on the basis of one-sided viewpoint. For this reason, we have proposed a data-driven and the deep learning based CDN recommendation framework for ICPs. The contributions lie in: 1) A three-tier CDN recommendation framework is presented to achieve data-driven and the deep learning based recommendation service. 2) A CDN recommendation model is built based on the deep neural network, which improves the efficiency of the recommendation service and satisfies the personalized demand. 3) A prototype system is developed and deployed on the real-world large-scale Internet in China. Experimental results demonstrate that the correctness of the recommendation results is up to 91%, and degree of satisfaction reached 80%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Yin H, Qiao B, Luo Y, Tian C, Yang YR (2015) Demystifying commercial content delivery networks in China. Concurrency and Computation Practice and Experience 27(13):3523–3538

    Article  Google Scholar 

  2. Adhikari V, Guo Y, Hao F, Varvello M, Hilt V, Steiner M, Zhang ZL (2012) Unreeling netflix: Understanding and improving multi-cdn movie delivery. In: 2012 Proceedings IEEE INFOCOM, pp1620–1628

  3. Adhikari V, Guo Y, Hao F, Varvello M, Hilt V, Steiner M, Zhang ZL (2012) A tale of three cdns: An active measurement study of hulu and its cdns. In: IEEE conference on computer communications workshops (INFOCOM WKSHPS), pp 7–12

  4. Liu H H, Wang Y, Yang Y R, Wang H, Tian C (2012) Optimizing cost and performance for content multihoming. In: Proceedings of the ACM SIGCOMM 2012 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, ser. SIGCOMM ’12. ACM, New York, pp 371–382

  5. Su A J, Choffnes D R, Kuzmanovic A, Bustamante F E (2009) Drafting behind akamai: inferring network conditions based on cdn redirections. IEEE/ACM Trans. Netw. 17(6):1752–1765

    Article  Google Scholar 

  6. CDN expert. http://cdnexpertonline.com/node/45

  7. Krishnamurthy B, Wills C, Zhang Y, Stoica I, Zhang H (2001) On the use and performance of content distribution networks. Proceedings of the ACM SIGCOMM internet measurement workshop 2001

  8. DeepField, First data on changing netflix and CDN market share. 2012. http://www.deepfield.net/2012/06/first-data-on-changing-netflix-and-cdn-market-share/

  9. Yu T, Tian C, Jiang H, Liu W Measurements and analysis of an unconstrained user generated content system. In: 2011 IEEE International Conference on Communications (ICC), pp 1–5

  10. Johnson KL, Carr JF, Day MS, Kaashoek MF (2001) The measured performance of content distribution networks. Comput Commun 24(2):202–206

    Article  Google Scholar 

  11. Huang C, Wang A, Li J, Ross KW (2008) Measuring and evaluating large-scale CDNs. In: Proceedings of the 2008 ACM SIGCOMM conference on internet measurement conference

  12. Triukose S, Wen Z, Rabinovich M (2011) Measuring a commercial content delivery network. In: Proceedings of WWW 2011

  13. Wang YA, Huang C, Li J, Ross KW (2011) Estimating the performance of hypothetical cloud service deployments: a measurement-based approach

  14. Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering 17(6), pp 734–749

  15. Zhang S, Yao L, Sun A (2017) Deep learning based recommender system: A survey and new perspectives. ACM J Comput Cult 1(1):35

    Google Scholar 

  16. Gomez-Uribe CA, Hunt N (2016) The netflix recommender system: Algorithms, business value, and innovation. ACM Trans Manag Inf Syst (TMIS) 6(4):13

    Google Scholar 

  17. Davidson J, Liebald B, Liu J, Nandy P, Vleet TV, Gargi U, Gupta S, He Y, Lambert M, Livingston B, Sampath D (2010) The YouTube video recommendation system. In: Proceedings of the 4th ACM conference on recommender systems (RecSys 10). ACM, New York, pp 293–296

  18. Burke R (2002) Hybrid recommender systems: Survey and experiments. User Model User-Adap Inter 12 (4):331–370

    Article  MATH  Google Scholar 

  19. McNee SM, Riedl J, Konstan JA (2006) Being accurate is not enough: How accuracy metrics have hurt recommender systems. In: CHI’06 extended abstracts on human factors in computing systems (CHI EA’06). ACM, New York, pp 1097–1101

  20. Vargas S, Castells P (2011) Rank and relevance in novelty and diversity metrics for recommender systems. In: Proceedings of the 5th ACM conference on Recommender systems. ACM, pp 109–116

  21. Cheng H-T, Koc L, Harmsen J, Shaked T, Chandra T, Aradhye H, Anderson G, Corrado G, Chai W, Ispir M et al (2016) Wide and deep learning for recommender systems. In: Proceedings of the 1st workshop on deep learning for recommender systems. ACM, pp 7–10

  22. Covington P, Adams J, Sargin E (2016) Deep neural networks for youtube recommendations. In: Proceedings of the 10th ACM conference on recommender systems. ACM, pp 191–198

  23. Okura S, Tagami Y, Ono S, Tajima A (2017) Embedding-based news recommendation for millions of users. In: Proceedings of the 23th ACM SIGKDD international conference on knowledge discovery and data mining. ACM

  24. He X, Liao L, Zhang H, Nie L, Hu X, Chua T-S (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on World Wide Web. International World Wide Web conferences steering committee, pp 173–182

  25. Wang X, He X, Nie L, Chua T-S (2017) Item silk road: Recommending items from information domains to social users. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval

  26. Lian J, Zhang F, Xie X, Sun G (2017) CCCFNet: A content-boosted collaborative filtering neural network for cross domain recommender systems. In: Proceedings of the 26th international conference on World Wide Web companion. International World Wide Web conferences steering committee, pp 817–818

  27. Alashkar T, Jiang S, Wang S, Fu Y (2017) Examples-rules guided deep neural network for makeup recommendation. In AAAI, pp 941–947

  28. Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge

    MATH  Google Scholar 

  29. Kingma DP, Ba J (2014) Adam: A Method for Stochastic Optimization. Computer Science

  30. Alexa (2017) Alexa’s top 100 site ranking for China. http://www.alexa.cn/

  31. Chen M, Xu Z, Weinberger K, Sha F (2012) Marginalized denoising autoencoders for domain adaptation. arXiv:1206.4683

  32. Zhang H, Shen F, Liu W, He X, Luan H, Chua T. -S. (2016) Discrete collaborative filtering. In: SIGIR pp.325-334

  33. Aditya P H, Budi I, Munajat Q (2017) A comparative analysis of memory-based and model-based collaborative filtering on the implementation of recommender system for E-commerce in Indonesia: A case study PT X. International Conference on Advanced Computer Science and Information Systems (ICACSIS), pp 303–308

  34. Lousame F P, Sanchez E (2009) A taxonomy of collaborative-based recommender systems. Web Person. in Intel. Environ., SCI 229, pp 81–117

  35. Liu C, Wu F (2013) The sutdy and implementation of web user mining system based on the similarity of words. Comput Sci Appl 3(4):232–238

    Google Scholar 

  36. Su Z, Xu H, Zhang D, Xu Y (2014) TChinese sentiment classification using a neural network tool- Word2vec. In: International conference on multisensor fusion & information integration for intelligent systems pp 1–6

Download references

Acknowledgment

This work was supported in part by the National Natural Science Foundation of China under Grant no.61672-318 and 61631013, in part by the National Key Research and Development Program under Grant no.2016YFB10-00102.

Author information

Authors and Affiliations

  1. The Computer Science and Technology Department at Tsinghua University, Beijing, 10084, China

    Bo Qiao

  2. The Research Institute of Information Technology (RIIT) at Tsinghua University, Beijing, 10084, China

    Hao Yin

Authors
  1. Bo Qiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hao Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bo Qiao.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection: Special Issue on Big Data and Smart Computing in Network Systems

Guest Editors: Jiming Chen, Kaoru Ota, Lu Wang, and Jianping He

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qiao, B., Yin, H. A data-driven and the deep learning based CDN recommendation framework for ICPs. Peer-to-Peer Netw. Appl. 12, 1445–1453 (2019). https://doi.org/10.1007/s12083-018-0673-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-018-0673-x

Keywords

Search

Navigation