Enhanced Streaming Services in a Content Distribution Network
IEEE Internet Computing
Ant Colony Optimization
Modeling and performance analysis of BitTorrent-like peer-to-peer networks
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
An analysis of live streaming workloads on the internet
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Rarest first and choke algorithms are enough
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
Invited review: A comparative analysis of several asymmetric traveling salesman problem formulations
Computers and Operations Research
GoalBit: the first free and open source peer-to-peer streaming network
Proceedings of the 5th International Latin American Networking Conference
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Will IPTV ride the peer-to-peer stream? [Peer-to-Peer Multimedia Streaming]
IEEE Communications Magazine
A study of real-time packet video quality using random neural networks
IEEE Transactions on Circuits and Systems for Video Technology
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The client-server architecture is still popular due to its high predictable service and performance. However, it is not bandwidth scalable. An alternative setup for Internet video-streaming is offered by the peer-to-peer architecture, in which peers are servers as well as clients. Peers basically communicate in a three-level based policy. First, they meet other peers with common interests: this is called swarming. Then, each peer selects a small number of them for cooperation, called the peer selection strategy. In the last step peers cooperate sending pieces, defining the piece selection strategy. This paper is focused on piece selection strategies. We propose an in-depth analysis of a simple cooperative model. In this model the issue is to find the best order in which pieces should be obtained. In the first stage, we introduce a Combinatorial Optimization Problem (COP), which maximizes the average user experience for video streaming services, and has a permutation as the decision variable. Its hardness motivates us to approximately solve it via an Ant Colony Optimization-based heuristic. The main theoretical contributions are twofold: the introduction of a new piece selection strategy with better results in contrast with the ones found in the literature, and a systematic way of computing new piece selection strategies with high quality. The practical contribution is the incorporation of a new piece selection strategy in a live peer-to-peer streaming platform, with remarkable performance in relation with classical strategies.