Congestion avoidance and control
SIGCOMM '88 Symposium proceedings on Communications architectures and protocols
Random early detection gateways for congestion avoidance
IEEE/ACM Transactions on Networking (TON)
Analysis of windowing mechanisms with infinite-state stochastic Petri nets
ACM SIGMETRICS Performance Evaluation Review - Special issue on Stochastic Petri Nets
End-to-end internet packet dynamics
IEEE/ACM Transactions on Networking (TON)
Modeling TCP Reno performance: a simple model and its empirical validation
IEEE/ACM Transactions on Networking (TON)
Performance analysis of TCP connections sharing a congested Internet link
Performance Evaluation - Special issue on internet performance modelling
Performance Analysis of Communication Systems with Non-Markovian Stochastic Petri Nets
Performance Analysis of Communication Systems with Non-Markovian Stochastic Petri Nets
Performance Modelling with Deterministic and Stochostic Petri Nets
Performance Modelling with Deterministic and Stochostic Petri Nets
Organization-based analysis of web-object sharing and caching
USITS'99 Proceedings of the 2nd conference on USENIX Symposium on Internet Technologies and Systems - Volume 2
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Wide-area Internet traffic patterns and characteristics
IEEE Network: The Magazine of Global Internetworking
Analytical modeling of TCP flow in wireless LANs
Mathematical and Computer Modelling: An International Journal
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In this paper we describe how the completion time distribution for short TCP connections can be computed using Deterministic Stochastic Petri Net (DSPN) models of TCP protocol. A DSPN model of TCP is a representation of the finite state machine description of the TCP transmitter behavior, and provides an accurate description of the TCP dynamics. The DSPN requires as input only the packet loss probability, and the average round trip time for the TCP connections being considered. The proposed model has been validated by comparing it against simulation results in various network scenarios, thus proving that the model is accurate. Numerical results are presented to prove the flexibility and the potentialities of the proposed methodology.