Framework for replica placement over cooperative edge networks

  • Pingting Hao
  • Liang Hu
  • Jingyan Jiang
  • Xilong Che
  • Tong Li
  • Kuo Zhao
Original Research
  • 18 Downloads

Abstract

Multimedia traffic is increasing sharply as an important component of Internet traffic to consume the bandwidth with various applications. Although most existing studies on edge networks focus on guaranteeing the quality of service for users by decreasing the latency and cost, the volume of traffic has become notably large, and the state of the network is unstable. The cloud platform and Software-Defined Network (SDN), as new technologies, undertake several functions from the edge networks such that the edge networks can concentrate on the delivery part and make optimal decisions. In this paper, we propose Cloud Co-CDNs (C3), which a prototype that transfers the management part of edge networks to the cloud with the software-defined network, and cooperation among providers of edge networks is considered in the prototype. Next, we design a stochastic model for describing the workflow in the C3 and develop a heuristic algorithm for replica placement to trade off among the latencies that are produced by the delivery process, the updating process and the replication process. Finally, we evaluate the performances of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising.

Keywords

Cooperated content delivery networks (CDN) Software-defined network (SDN) Cloud computing Placement Queuing theory 

Notes

Acknowledgements

This work is funded by the National Key R&D Plan of China under Grant no. 2017YFA0604500, the National Sci-Tech Support Plan of China under Grant no. 2014BAH02F00, the National Natural Science Foundation of China under Grant no. 61701190, the Youth Science Foundation of Jilin Province of China under Grant no. 20160520011JH and 20180520021JH, the Youth Sci-Tech Innovation Leader and Team Project of Jilin Province of China under Grant no. 20170519017JH, and the Key Technology Innovation Cooperation Project of Government and University for the Whole Industry Demonstration under Grant no. SXGJSF2017-4.

References

  1. Aggarwal V, Chen X, Gopalakrishnan V et al (2011) Exploiting virtualization for delivering cloud-based IPTV services. In: IEEE Computer Communications Workshops, pp 637–641Google Scholar
  2. Choy S, Wong B, Simon G et al (2014) A hybrid edge-cloud architecture for reducing on-demand gaming latency. Multimedia Syst 20(5):503–519CrossRefGoogle Scholar
  3. Cisco visual networking index (2016) Global mobile data traffic forecast update, 2015–2020, white paperGoogle Scholar
  4. CloudFare (2017) https://www.cloudflare.com/
  5. Fan L, Lei X, Yang N, Duong TQ, Karagiannidis GK (2016) Secure multiple amplify-and-forward relaying with cochannel interference. IEEE J Select Topics Signal Process 10(8):1494–1505CrossRefGoogle Scholar
  6. Fan L, Lei X, Yang N, Duong TQ, Karagiannidis GK (2017) Secrecy cooperative networks with outdated relay selection over correlated fading channels. IEEE Trans Vehicular Technol 66(8):7599–7603CrossRefGoogle Scholar
  7. Gross D (2008) Fundamentals of queueing theory. WileyGoogle Scholar
  8. Jiang T, Chen X, Li J, Wong DS, Ma J, Liu JK (2015) Towards secure and reliable cloud storage against data re-outsourcing. Future Gener Comp Syst 52:86–94CrossRefGoogle Scholar
  9. Kangasharju J, Roberts J, Ross KW (2002) Object replication strategies in content distribution networks. Comput Commun 25(4):376–383CrossRefGoogle Scholar
  10. Kolisch R, Dahlmann A (2015) The dynamic replica placement problem with service levels in content delivery networks: a model and a simulated annealing heuristic. OR Spectrum 37(1):217–242MathSciNetCrossRefMATHGoogle Scholar
  11. Lai X, Zou W, Xie D, Li X, Fan L (2017) DF relaying networks with randomly distributed interferers. IEEE Access 5:18909–18917CrossRefGoogle Scholar
  12. Lange S, Gebert S, Zinner T et al (2015) Heuristic approaches to the controller placement problem in large scale SDN networks. IEEE Trans Netw Serv Manage 12(1):4–17CrossRefGoogle Scholar
  13. Limelight (2017) https://www.limelight.com/
  14. Lin W, Xu S, Li J, Xu L, Peng Z (2017a) Design and theoretical analysis of virtual machine placement algorithm based on peak workload characteristics. Soft Comput 21(5):1301–1314CrossRefMATHGoogle Scholar
  15. Lin W, Wu Z, Lin L, Wen A, Li J (2017b) An ensemble random forest algorithm for insurance big data analysis. IEEE Access 5:16568–16575CrossRefGoogle Scholar
  16. Lin W, Xu S, He L, Li J (2017c) Multi-resource scheduling and power simulation for cloud computing. Inf Sci 397:168–186CrossRefGoogle Scholar
  17. Liu B, Fan B, Xiao T et al (2015) Unsupervised dynamic fuzzy cognitive map. Tsinghua Sci Technol 20(3):285–292MathSciNetCrossRefGoogle Scholar
  18. Liu C, Sitaraman RK, Towsley D (2016) Go-with-the-Winner: Performance based client-side server selectionGoogle Scholar
  19. Meng W, Tischhauser E, Wang Q, Wang Y, Han J (2018) When intrusion detection meets Blockchain technology: a review. IEEE Access.  https://doi.org/10.1109/ACCESS.2018.2799854 Google Scholar
  20. Netflix Open Connect (2016) https://signup.netflix.com/openconnect
  21. Pacifici V, Dán G (2015) Distributed algorithms for content allocation in interconnected content distribution networks. In: 2015 IEEE Conference on Computer Communications (INFOCOM). IEEE, pp 2362–2370Google Scholar
  22. Pathan AMK, Buyya R (2007) A taxonomy and survey of content delivery networks. In: Grid Computing and Distributed Systems Laboratory, University of Melbourne, Technical Report, p 4Google Scholar
  23. Peng S, Wang G, Xie D (2017) Social influence analysis in social networking big data: opportunities and challenges. IEEE Netw 31(1):11–17CrossRefGoogle Scholar
  24. Rappaport A, Raz D (2013) Update aware replica placement. In: 2013 9th International Conference on Network and Service Management (CNSM). IEEE, pp 92–99Google Scholar
  25. Ren D, Chan SHG, Shi G et al (2014) Distributed joint optimization for large-scale video-on-demand. Comput Netw 75:86–98CrossRefGoogle Scholar
  26. Schwarz M, Sauer C, Daduna H et al (2006) M/M/1 queueing systems with inventory. Que Syst 54(1):55–78MathSciNetCrossRefMATHGoogle Scholar
  27. Tuncer D, Sourlas V, Charalambides M et al (2016) Scalable cache management for ISP-operated content delivery services. IEEE J Sel Areas Commun 34(8):2063–2076CrossRefGoogle Scholar
  28. Wang X, Tang S (2015) Bit-level soft-decision decoding of double and triple-parity reed-solomon codes through binary hamming code constraints. IEEE Commun Lett 19(2):135–138MathSciNetCrossRefGoogle Scholar
  29. Wang X, Ma X, Bai BM (2014) Design of efficiently encodable nonbinary LDPC codes for adaptive coded modulation. Sci Chin Inf Sci 57(2):1–11MATHGoogle Scholar
  30. Wang Y, Li K, Li K (2017) Partition scheduling on heterogeneous multicore processors for multi-dimensional loops applications. Int J Parallel Prog 45(4):827–852MathSciNetCrossRefGoogle Scholar
  31. Zhang Z, Xi H, Song C (2014) Dynamic optimal resource provisioning for VoD services under Amazon EC2’s pricing models. In: IEEE Control Conference, pp 5527–5532Google Scholar
  32. Zhang Y, Cui G, Wang Y et al (2015) An optimization algorithm for service composition based on an improved FOA. Tsinghua Sci Technol 20(1):90–99MathSciNetCrossRefGoogle Scholar
  33. Zhou J, Hu L, Wang F et al (2013) An efficient multidimensional fusion algorithm for IoT data based on partitioning. Tsinghua Sci Technol 18(4):369–378CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Pingting Hao
    • 1
  • Liang Hu
    • 1
  • Jingyan Jiang
    • 1
  • Xilong Che
    • 1
  • Tong Li
    • 2
  • Kuo Zhao
    • 1
  1. 1.Department of Computer ScienceJiLin UniversityChangchunChina
  2. 2.Department of Computer ScienceGuangzhou UniversityGuangzhouChina

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