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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >A Trace-Driven Approach to Evaluate the Scalability of P2P-Based Video-on-Demand Service
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A Trace-Driven Approach to Evaluate the Scalability of P2P-Based Video-on-Demand Service

机译：跟踪驱动的方法来评估基于P2P的视频点播服务的可扩展性

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摘要

Peer-to-Peer (P2P) networks have emerged as one of the most promising approaches to improve the scalability of Video-on-Demand (VoD) service over Internet. However, despite a number of architectures and streaming protocols have been proposed in past years, there is few work to study the practical performance of P2P-based VoD service especially in consideration of real user behavior which actually has significant impact on system scalability. Therefore, in this paper, we first characterize the user behavior by analyzing a large amount of real traces from a popular VoD system supported by the biggest television station in China, cctv.com. Then we ex-amine the practical scalability of P2P-based VoD service through extensive trace-driven simula-tion under a general system framework. The results show that P2P networks scale well in provid-ing VoD service under real user behavior by obtaining a considerable good cache hit ratio. Moreover, it is observed that adopting hard cache at client side help achieves better system scal-ability than that with soft cache. We also identify the impact of various aspects of user behavior upon system scalability through detailed simulation. We believe that our study will shine insight-ful light on the understanding of practical scalability of P2P-based VoD service and be helpful to future system design and optimization.
机译：对等（P2P）网络已经成为提高Internet上点播视频（VoD）服务可伸缩性的最有希望的方法之一。但是，尽管在过去几年中已经提出了许多体系结构和流协议，但是很少有研究基于P2P的VoD服务的实际性能的工作，特别是考虑到实际对系统可伸缩性有重大影响的实际用户行为。因此，在本文中，我们首先通过分析由中国最大的电视台cctv.com支持的流行VoD系统中的大量真实痕迹来表征用户行为。然后，我们在一个通用系统框架下，通过广泛的跟踪驱动模拟来研究基于P2P的VoD服务的实际可伸缩性。结果表明，通过获得相当好的缓存命中率，P2P网络可以在真实用户行为下很好地提供VoD服务。此外，可以观察到，与软缓存相比，在客户端采用硬缓存有助于实现更好的系统可扩展性。我们还将通过详细的仿真来确定用户行为各个方面对系统可伸缩性的影响。我们相信我们的研究将为理解基于P2P的VoD服务的实际可扩展性提供深刻的见解，并有助于将来的系统设计和优化。

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