Traffic engineering of peer-assisted content delivery network with content-oriented incentive mechanism

Naoya Maki, Takayuki Nishio, Ryoichi Shinkuma, Tatsuya Mori, Noriaki Kamiyama, Ryoichi Kawahara, Tatsuro Takahashi

研究成果: Article査読

4 被引用数 (Scopus)

抄録

In content services where people purchase and download large-volume contents, minimizing network traffic is crucial for the service provider and the network operator since they want to lower the cost charged for bandwidth and the cost for network infrastructure, respectively. Traffic localization is an effective way of reducing network traffic. Network traffic is localized when a client can obtain the requested content files from other a near-by altruistic client instead of the source servers. The concept of the peer-assisted content distribution network (CDN) can reduce the overall traffic with this mechanism and enable service providers to minimize traffic without deploying or borrowing distributed storage. To localize traffic effectively, content files that are likely to be requested by many clients should be cached locally. This paper presents a novel traffic engineering scheme for peer-assisted CDN models. Its key idea is to control the behavior of clients by using content-oriented incentive mechanism. This approach enables us to optimize traffic flows by letting altruistic clients download content files that are most likely contributed to localizing traffic among clients. In order to let altruistic clients request the desired files, we combine content files while keeping the price equal to the one for a single content. This paper presents a solution for optimizing the selection of content files to be combined so that cross traffic in a network is minimized. We also give a model for analyzing the upper-bound performance and the numerical results.

本文言語English
ページ(範囲)2860-2869
ページ数10
ジャーナルIEICE Transactions on Information and Systems
E95-D
12
DOI
出版ステータスPublished - 2012 12
外部発表はい

ASJC Scopus subject areas

  • ソフトウェア
  • ハードウェアとアーキテクチャ
  • コンピュータ ビジョンおよびパターン認識
  • 電子工学および電気工学
  • 人工知能

フィンガープリント

「Traffic engineering of peer-assisted content delivery network with content-oriented incentive mechanism」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル