Robust and optimal control
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
A Fluid-Diffusive Approach for Modelling P2P Systems
MASCOTS '06 Proceedings of the 14th IEEE International Symposium on Modeling, Analysis, and Simulation
The Fluid Limit of an Overloaded Processor Sharing Queue
Mathematics of Operations Research
Efficient simulation of large-scale p2p networks: packet-level vs. flow-level simulations
Proceedings of the second workshop on Use of P2P, GRID and agents for the development of content networks
Global stability of Peer-to-Peer file sharing systems
Computer Communications
A queueing system for modeling a file sharing principle
SIGMETRICS '08 Proceedings of the 2008 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
IEEE/ACM Transactions on Networking (TON)
Modeling of epidemic diffusion in peer-to-peer file-sharing networks
BioADIT'06 Proceedings of the Second international conference on Biologically Inspired Approaches to Advanced Information Technology
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In this paper we analyze the dynamics of P2P file exchange networks, considering both queueing and fluid models. In such systems, the service rate depends on one mostly fixed component (servers or seeders), and another that scales with the number of peers present. We analyze a class of M/G Processor Sharing queues that describe populations and residual workloads in this situation, characterizing its stationary regime. It is shown that, under a law of large numbers scaling, the system behaves as a M/G/1 or a shifted M/G/∞ queue, depending on whether the server or peer contribution becomes dominant. We also consider fluid models for populations and residual workloads in the form of a partial differential equation, and establish connections with the queueing approach. This method provides broadly applicable results on stability, variability and transient performance, which we validate against packet simulations, showing improvement with respect to earlier models.