Peer-to-Peer Networking and Applications

, Volume 8, Issue 2, pp 320–335 | Cite as

Locality-aware streaming in hybrid P2P-cloud CDN systems

  • Jian ZhaoEmail author
  • Chuan Wu
  • Xiaojun Lin


The cloud CDN has been exploited as a cost-effective and elastic way for video streaming, where the content providers only pay for resources (e.g., bandwidth, storage, Web service) that they use. As a server-client solution, the cost on the content providers, especially the bandwidth cost for delivering videos from the cloud to the users, is still high. To further mitigate the content distribution cost, it is promising to deploy the P2P streaming technology in conjunction with a cloud CDN, to construct a hybrid P2P-cloud CDN system. In this paper, we focus on optimal, locality-aware video-on-demand streaming solutions in a hybrid P2P-cloud CDN system, which achieve the best tradeoff between the costly bandwidth consumption on the cloud servers and the undesirable inter-ISP traffic incurred in the entire system. Especially, we characterize the demanded upload bandwidth in the cloud CDN and the incurred inter-ISP traffic through a number of stochastic and optimization models. We first apply a loss network model to derive the needed bandwidth capacity in the cloud CDN under any given chunk distribution pattern among the peer caches and any streaming request dispatching strategy among the ISPs, and derive the optimal peer caching and request dispatching strategies which minimize the bandwidth demand on the cloud CDN. We then investigate the necessary volume of inter-ISP traffic required to achieve the minimum cloud bandwidth, as well as the impact of limiting the inter-ISP traffic on the cloud bandwidth consumption. Based on the fundamental insights from our models and analytical results, we design a locality-aware, hybrid P2P-cloud streaming protocol, and validate its performance using extensive simulations under realistic settings.


Hybrid P2P-cloud Locality-awareness Stochastic optimization VoD streaming 


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Computer ScienceThe University of Hong KongHong KongPeople’s Republic of China
  2. 2.School of Electrical and Computer EngineeringPurdue UniversityWest LafayetteUSA

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