Stochastic Estimator-Based Wireless Traffic Control Schemes
Abstract
Recent works on Available Bit Rate (ABR) traffic control have generated efficient control schemes for ABR traffic on Asynchronous Transfer Mode (ATM) network. This study examines the improved performance envisaged if these control schemes adjust dynamically to the varying ABR bandwidth capacity in a stochastic manner instead of conventional deterministic approach .The performance difference between setting explicit rate deterministically for transmitting ABR sources and doing the same stochastically using a learning automaton is of particular interest. The learning automaton used is the Stochastic Estimator Learning Automaton (SELA). The performance difference is measured by comparing the congestion levels of the SELA-based control scheme with the reference deterministic control mechanism. Simulation results show that the stochastic estimator gives a better performance. The higher average congestion level experienced by the conventional deterministic approach is mainly due to the propagation time delay in the closed-loop feedback control schemes.
DOI: https://doi.org/10.3844/jcssp.2007.918.923
Copyright: © 2007 Francis Joseph Ogwu, Mohammad Talib and Ganiyu Adesola Aderounmu. This is an open access article distributed under the terms of the
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Keywords
- Learning algorithm and training
- network
- application control
- deterministic
- estimation
- performance evaluation
- simulation
- network architecture
- feedback control
- propagationtime
- stochastic control
- random sampling
- equation
- reinforcement learning
- convergence time
- robust