Multimedia Tools and Applications

, Volume 73, Issue 3, pp 1663–1686 | Cite as

CBC: Caching for cloud-based VOD systems

  • Weizhan Zhang
  • Zhichao Mo
  • Cheng Chen
  • Qinghua Zheng


Cloud-based video on demand (VOD) service is a promising next-generation media streaming service paradigm. Being a resource-intensive application, how to maximize resource utilization is a key issue of designing such an application. Due to the special cloud-based VOD system architecture consisting of cloud storage cluster and media server cluster, existing techniques such as traditional caching strategies are inappropriate to be adopted by a cloud-based VOD system directly in practice. Therefore, in this study, we have proposed a systemic caching scheme, which seamlessly integrates a caching algorithm and a cache deployment algorithm together to maximize the resources utilization of cloud-based VOD system. Firstly, we have proposed a cloud-based caching algorithm. The algorithm models the cloud-based VOD system as a multi-constraint optimization problem, so as to balance the resource utilization between cloud storage cluster and media server cluster. Secondly, we have proposed a cache deployment algorithm. The algorithm further manages the bandwidth and cache space resource utilization inside the media server cluster in a more fine-grained manner, and achieves load balancing performance. Our evaluation results show that the proposed scheme enhances the resource utilization of the cloud-based VOD system under resource-constrained situation, and cuts down the reject ratio of user requests.


Caching Cloud computing VOD 



The research was supported in part by National Science Foundation of China under Grant Nos. 61103239, 61221063; National High Technology Research and Development Program 863 of China under Grant No. 2012AA011003; Cheung Kong Scholar’s Program; the Fundamental Research Funds for the Central Universities; Key Projects in the National Science and Technology Pillar Program under Grant Nos. 2012BAH16F02.


  1. 1.
    Abboud O, Zinner T, Pussep K, Al-Sabea S, Steinmetz R (2011) On the impact of quality adaptation in svc-based p2p video-on-demand systems. In: Proc. of the second annual ACM conference on multimedia systems, pp 223–232Google Scholar
  2. 2.
    Aggarwal V, Chen X, Gopalakrishnan V, Jana R, Ramakrishnan K (2011) Vaishampayan, V.: Exploiting virtualization for delivering cloud-based iptv services. In: Proc. of IEEE INFOCOM workshops, pp 637–641Google Scholar
  3. 3.
    Amazon (2012) Accessed 30 Dec 2012
  4. 4.
    Amazon (2012) Accessed 30 Dec 2012
  5. 5.
    Amazon (2012) Accessed 30 Dec 2012
  6. 6.
    Armbrust M, Fox A, Griffith R, Joseph A, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I, et al (2010) A view of cloud computing. Commun ACM 53(4):50–58CrossRefGoogle Scholar
  7. 7.
    Carlsson N, Eager D (2010) Server selection in large-scale video-on-demand systems. ACM Trans Multimedia Comput Commun Appl (TOMCCAP) 6(1):1–26 (2010)CrossRefGoogle Scholar
  8. 8.
    Chen S, Wang H, Zhang X, Shen B, Wee S (2005) Segment-based proxy caching for internet streaming media delivery. IEEE Multimedia 12(3), 59–67CrossRefGoogle Scholar
  9. 9.
    Choi J, Reaz A, Mukherjee B (2012) A survey of user behavior in vod service and bandwidth-saving multicast streaming schemes. IEEE Commun Surv Tutor 14(1):156–169CrossRefGoogle Scholar
  10. 10.
    Dan A, Sitaram D (1996) A generalized interval caching policy for mixed interactive and long video workloads. In: Proc. of multimedia computing and networking (MMCN) conference, pp 344–351Google Scholar
  11. 11.
    Garcia A, Kalva H, Furht B (2010) A study of transcoding on cloud environments for video content delivery. In: Proc. of the 2010 ACM multimedia workshop on mobile cloud media computing, pp 13–18Google Scholar
  12. 12.
    Huang Y, Fu T, Chiu D, Lui J, Huang C (2008) Challenges, design and analysis of a large-scale p2p-vod system. ACM SIGCOMM Comput Commun Rev 38(4):375–388CrossRefGoogle Scholar
  13. 13.
    Jha R, Dalal U (2011) A performance comparison with cost for qos application in on-demand cloud computing. In: Proc. of IEEE recent advances in intelligent computational systems (RAICS), pp 11–18Google Scholar
  14. 14.
    Kangasharju J, Hartanto F, Reisslein M, Ross K (2002) Distributing layered encoded video through caches. IEEE Trans Comput 51(6):622–636CrossRefGoogle Scholar
  15. 15.
    Lau P, Park S, Yoon J, Lee J (2010) Pay-as-you-use on-demand cloud service: an iptv case. In: Proc. of 2010 international conference on electronics and information engineering (ICEIE), pp 272–276Google Scholar
  16. 16.
    Li B, Yin H (2007) Peer-to-peer live video streaming on the internet: issues, existing approaches, and challenges. IEEE Commun Mag 45(6):94–99CrossRefGoogle Scholar
  17. 17.
    Li H, Zhong L, Liu J, Li B, Xu K (2011) Cost-effective partial migration of vod services to content clouds. In: Proc. of 2011 IEEE international conference on cloud computing (CLOUD), pp. 203–210Google Scholar
  18. 18.
    Liu J, Xu J (2004) Proxy caching for media streaming over the internet. IEEE Commun Mag 42(8):88–94CrossRefGoogle Scholar
  19. 19.
    Meskill B, Davy A, Jennings B (2011) Server selection and admission control for ip-based video on demand using available bandwidth estimation. In: Proc. of 2011 IEEE 36th conference on local computer networks (LCN), pp. 255–258Google Scholar
  20. 20.
    Nan X, He Y, Guan L (2011) Optimal resource allocation for multimedia cloud based on queuing model. In: Proc. of IEEE 13th international workshop on multimedia signal processing (MMSP), pp 1–6Google Scholar
  21. 21.
    Netflix (2012) Accessed 30 Dec 2012
  22. 22.
    Niu D, Feng C, Li B (2012) Pricing cloud bandwidth reservations under demand uncertainty. In: Proc. of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on measurement and modeling of computer systems, pp 151–162Google Scholar
  23. 23.
    Niu D, Xu H, Li B, Zhao S (2012) Quality-assured cloud bandwidth auto-scaling for video-on-demand applications. In: Proc. of IEEE INFOCOM, vol 12, pp 460–468Google Scholar
  24. 24.
    Papakos P, Capra L, Rosenblum D (2010) Volare: context-aware adaptive cloud service discovery for mobile systems. In: Proc. of the 9th International workshop on adaptive and reflective middleware, pp. 32–38Google Scholar
  25. 25.
    Pawar S, El Rouayheb S, Zhang H, Lee K, Ramchandran K (2011) Codes for a distributed caching based video-on-demand system. Extended Version http://basics. eecs. Accessed 30 Dec 2012
  26. 26.
    Shu C, Zhang X (2011) Research on virtualization-based video-on-demand services architecture. In: Proc. of SPIE, pp 83, 491A–83, 491A9Google Scholar
  27. 27.
    Tewari R, Vin H, Dan A, Sitaram D (1998) Resource-based caching for web servers. In: Proc. SPIE/ACM conference on multimedia computing and networking, pp 191–204Google Scholar
  28. 28.
    Tu W, Steinbach E, Muhammad M, Li X (2009) Proxy caching for video-on-demand using flexible starting point selection. IEEE Trans Multimedia 11(4):716–729CrossRefGoogle Scholar
  29. 29.
    Wu Y, Wu C, Li B, Qiu X, Lau F (2011) Cloudmedia: when cloud on demand meets video on demand. In: Proc. of IEEE ICDCS, pp 268–277Google Scholar
  30. 30.
    Zhang Q, Lin Y, Wang Z (2012) Cost-effective capacity migration of peer-to-peer social media to clouds. Peer Peer Netw Appl 27(5):484–495Google Scholar
  31. 31.
    Zhang W, Zheng Q (2011) Multi-channel live streaming in service overlay network. Multimed Tools Appl 53(1):97–117CrossRefGoogle Scholar
  32. 32.
    Zhu W, Luo C, Wang J, Li S (2011) Multimedia cloud computing. IEEE Signal Process Mag 28(3):59–69CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Weizhan Zhang
    • 1
  • Zhichao Mo
    • 1
  • Cheng Chen
    • 1
  • Qinghua Zheng
    • 1
  1. 1.SKLMS Lab, Department of Computer Science and TechnologyXian Jiaotong UniversityXianChina

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