Optimizing network objectives in collaborative content distribution

One of the important trends is that the Internet will be used to transfer content on more and more massive scale. Collaborative distribution techniques such as swarming and parallel download have been invented and effectively applied to end-user file-sharing or media-streaming applications, but most...

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Vydáno v:Computer networks (Amsterdam, Netherlands : 1999) Ročník 91; s. 244 - 261
Hlavní autoři: Zheng, Xiaoying, Xia, Ye
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 14.11.2015
Elsevier Sequoia S.A
Témata:
Algorithms
Bandwidth allocation
Collaboration
Congestion
Congestion control
Content distribution
Convergence
Digital media
Distribution
Distribution management
Downloading
End users
Information sharing
Internet
Mass media effects
Networks
Objectives
Optimization
Optimization algorithms
Peer-to-peer network
Receivers
Receiving
Server selection
Streaming media
Studies
Swarming
Traffic congestion
Wireless networks
Peer-to-peer network
Bandwidth allocation
Congestion control
Content distribution
Server selection
Optimization
ISSN:1389-1286, 1872-7069
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Shrnutí:One of the important trends is that the Internet will be used to transfer content on more and more massive scale. Collaborative distribution techniques such as swarming and parallel download have been invented and effectively applied to end-user file-sharing or media-streaming applications, but mostly for improving end-user performance objectives. In this paper, we consider the issues that arise from applying these techniques to content distribution networks for improving network objectives, such as reducing network congestion. In particular, we formulate the problem of how to make many-to-many assignment from the sending nodes to the receivers and allocate bandwidth for every connection, subject to the node capacity and receiving rate constraints. The objective is to minimize the worst link congestion over the network, which is equivalent to maximizing the distribution throughput, or minimizing the distribution time. The optimization framework allows us to jointly consider server load balancing, network congestion control, as well as the requirement of the receivers. We develop a special, diagonally-scaled gradient projection algorithm, which has a faster convergence speed, and hence, better scalability with respect to the network size than a standard subgradient algorithm. We provide both a synchronous algorithm and a more practical asynchronous algorithm.
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ISSN:1389-1286
1872-7069
DOI:10.1016/j.comnet.2015.08.013