Open, Closed, and Mixed Networks of Queues with Different Classes of Customers
Journal of the ACM (JACM)
An analysis of Internet content delivery systems
ACM SIGOPS Operating Systems Review - OSDI '02: Proceedings of the 5th symposium on Operating systems design and implementation
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
Theory, Volume 1, Queueing Systems
Theory, Volume 1, Queueing Systems
Performance Evaluation - Performance 2005
The Delicate Tradeoffs in BitTorrent-like File Sharing Protocol Design
ICNP '06 Proceedings of the Proceedings of the 2006 IEEE International Conference on Network Protocols
Modeling QoS in P2P file-sharing with benign and malicious peers by stochastic activity networks
CCNC'10 Proceedings of the 7th IEEE conference on Consumer communications and networking conference
Peer-to-peer video-on-demand with scalable video coding
Computer Communications
Multi-stream 3D video distribution over peer-to-peer networks
Image Communication
Stochastic modelling of peer-assisted VoD streaming in managed networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Modeling and optimizing Random Walk content discovery protocol over mobile ad-hoc networks
Performance Evaluation
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Recent development of peer-to-peer (P2P) services (e.g. streaming, file sharing, and storage) systems introduces a new type of queue systems not studied before. In these new systems, both job and server arrive and depart randomly. The server dynamics may or may not correlate to the job dynamics. Motivated by these observations, we develop queuing models for P2P service systems and a taxonomy for different variations of these queueing models. For several basic classes of these systems, we show that they are stable, i.e. all arriving job will be served and cleared in finite time, if the average workload does not exceed the average system service capacity. Numerical experiments verify our results, and indicate that higher server dynamics lead to less time a job spends in the system on average.