Optimizing network objectives in collaborative content distribution

• Published in: Computer networks (Amsterdam, Netherlands : 1999) Vol. 91; pp. 244 - 261
• Main Authors: Zheng, Xiaoying, Xia, Ye
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 14.11.2015; Elsevier Sequoia S.A
• Subjects: Algorithms; Bandwidth allocation; Collaboration; Congestion; Congestion control; Content distribution; Digital media; Distribution management; Downloading; End users; Information sharing; Networks; 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
• DOI: 10.1016/j.comnet.2015.08.013

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.