How good are deterministic models for analyzing congestion control in delayed stochastic networks?
Lestas I. and Vinnicombe G.
Proc. 43rd IEEE Conference on Decision and Control, December 2004Abstract
We investigate the regime where instability in deterministic fluid
flow models for congestion control analysis in data
networks corresponds to a significant increase in the variance of the
flow in stochastic networks. This is shown to be the case when there
are large number of packets in flight with small queue thresholds. The
analysis is carried out by modelling an M/M/1 queue with delayed
feedback as a stochastic hybrid system and analyzing the
transient probability distribution of the states with partial
differential equations. We also introduce a deterministic nonlinear
dynamic queue model that captures the dynamics of the stochastic
feedback system. Most of the literature on congestion
control analysis using deterministic models, is currently based on
queueing models that are valid in one of the extreme cases
of negligible queueing delays relative to propagation delays (these
are modelled with static functions) or
never emptying queues (modelled as integrators). The proposed model is
shown to be valid both in these extreme conditions, as well as
intermediate regimes of large delays, emptying queues and significant
queue dynamics.
BibTex Entry
- @InProceedings{,
- author = {Lestas I. and Vinnicombe G.},
- title = {How good are deterministic models for analyzing congestion control in delayed stochastic networks?},
- booktitle = {Proc. 43rd IEEE Conference on Decision and Control},
- month = {December},
- year = {2004}
- }
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