The Journal of Supercomputing

, Volume 66, Issue 2, pp 700–720 | Cite as

Churn-aware optimal layer scheduling scheme for scalable video distribution in super-peer overlay networks

  • Yong-Hyuk MoonEmail author
  • Jeong-Nyeo Kim
  • Chan-Hyun Youn


To model a layered video streaming system in super-peer overlay networks that faces with heterogeneity and volatility of peers, we formulate a layer scheduling problem from understanding some constraints such as layer dependency, transmission rule, and bandwidth heterogeneity. To solve this problem, we propose a new layer scheduling algorithm using a real-coded messy genetic algorithm, providing a feasible solution with low complexity in decision. We also propose a peer-utility-based promotion algorithm that selects the most qualified neighbor to guarantee the sustained quality of streaming despite high intensity of churn. Simulation results show that the proposed layer scheduling scheme can achieve the most near-optimal solutions compared to the four conventional scheduling heuristics in the average streaming ratio. It also highly outperforms those with different peer selection strategies in terms of the average bandwidth (6.9 % higher at least) and the variation of utilization (11.3 % lower at least).


Content delivery Layer-coded video Streaming Churn resilience Peer-to-peer network Genetic algorithm 



This research was equally supported by R&D programs of MEST/NRF [2012-0020522, the Next-Generation Information Computing Development Program], and MKE/KEIT [10039260, Integrated development environment for personal, biz-customized open mobile cloud service and Collaboration tech for heterogeneous devices on server]. This research also was supported by IT R&D program of MKE/KEIT [10038768, The Development of Supercomputing System for the Genome Analysis].


  1. 1.
    Ibaraki T, Katoh N (1988) Resource allocation problems: algorithmic approaches. MIT Press, Cambridge zbMATHGoogle Scholar
  2. 2.
    Stutzbach D, Rejaie R (2005) Understanding churn in peer-to-peer network. In: Proceedings of the ACM Internet measurement conference (ACM IMC) Google Scholar
  3. 3.
    Zhou X, Ge Y, Chen X, Jing Y, Sun W (2012) A distributed cache based reliable service execution and recovery approach in MANETs. J Converg 3(1):5–12 Google Scholar
  4. 4.
    Pai V, Kumar K, Tamilmani K, Sambamurthy V, Mohr AE Mohr EE (2005) Chainsaw: eliminating trees from overlay multicast. In: Proceedings IEEE INFOCOM Google Scholar
  5. 5.
    Zhang X, Liut J, Lis B, Yum T-SP (2005) Coolstreaming/DONet: a data-driven overlay network for efficient live media streaming. In: Proceedings IEEE INFOCOM Google Scholar
  6. 6.
    Agarwal V, Rejaie R (2005) Adaptive multi-source streaming in heterogeneous peer-to-peer networks. In: Proceedings of the multimedia computing and networking (MMCN) Google Scholar
  7. 7.
    Zhang M, Chen C, Xiong Y, Zhang Q, Yang S (2007) Optimizing the throughput of data-driven based streaming in heterogeneous overlay network. In: Proceedings of ACM multimedia modeling (ACM MMM’07) Google Scholar
  8. 8.
    Xiao L, Zhuang Z, Liu Y (2005) Dynamic layer management in superpeer architectures. IEEE Trans Parallel Distrib Syst 16(1):1078–1091 CrossRefGoogle Scholar
  9. 9.
    Li J-S, Chao C-H (2010) An efficient superpeer overlay construction and broadcasting scheme based on perfect difference graph. IEEE Trans Parallel Distrib Syst 21(5):594–606 CrossRefGoogle Scholar
  10. 10.
    Kim H, Lee S, Lee J, Lee Y (2010) Reducing channel capacity for scalable video coding in a distributed network. ETRI J 32(6):863–870 CrossRefGoogle Scholar
  11. 11.
    Mastroianni C, Cozza P, Talia D, Kelley I, Taylor I (2009) A scalable super-peer approach for public scientific computation. Future Gener Comput Syst 25(3):213–223 CrossRefGoogle Scholar
  12. 12.
    Schwarz H, Marpe D, Wiegand T (2007) Overview of the scalable video coding extension of the H.264/AVC standard. IEEE Trans Circuits Syst Video Technol 17(9):1103–1120 CrossRefGoogle Scholar
  13. 13.
    Goldberg DE, Korb B, Deb K (1989) Messy genetic algorithms: motivation, analysis, and first results. Complex Syst 3:493–530 MathSciNetzbMATHGoogle Scholar
  14. 14.
    Wei Q, Qin T, Fujita S (2011) A two-level caching protocol for hierarchical peer-to-peer file sharing systems. J Converg 2(1):11–16 Google Scholar
  15. 15.
    Luo H, Shyu M-L (2011) Quality of service provision in mobile multimedia—a survey. Hum-Cent Comput Inf Sci. doi: 10.1186/2192-1962-1-5 Google Scholar
  16. 16.
    Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading zbMATHGoogle Scholar
  17. 17.
    Moon Y-H, Youn C-H (2012) Integrated approach towards aggressive state-tracking migration for maximizing performance benefit in distributed computing. Clust Comput. doi: 10.1007/s10586-011-0197-0 Google Scholar
  18. 18.
    Lobo FG, Goldberg DE, Pelikan M (2000) Time complexity of genetic algorithms on exponentially scaled problems. In: Proceedings of the genetic and evolutionary computation conference, pp 151–158 Google Scholar
  19. 19.
    Leonard D, Yao Z, Rai V, Loguinov D (2007) On lifetime-based node failure and stochastic resilience of decentralized peer-to-peer networks. IEEE/ACM Trans Netw 15(3):644–656 CrossRefGoogle Scholar
  20. 20.
    Ross SM (1996) Stochastic processes. Wiley, New York zbMATHGoogle Scholar
  21. 21.
    Aikebaier A, Enokido T, Takizawa M (2011) Trustworthy group making algorithm in distributed systems. Hum-Cent Comput Inf Sci. doi: 10.1186/2192-1962-1-6 Google Scholar
  22. 22.
    Overlay and peer-to-peer network simulation (OverSim) framework.

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Yong-Hyuk Moon
    • 1
    • 2
    Email author
  • Jeong-Nyeo Kim
    • 1
  • Chan-Hyun Youn
    • 3
  1. 1.Software Research LaboratoryElectronics and Telecommunications Research Institute (ETRI)DaejeonSouth Korea
  2. 2.Department of Information and Communications EngineeringKorea Advanced Institute of Science and Technology (KAIST)DaejeonSouth Korea
  3. 3.Department of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)DaejeonSouth Korea

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