Abstract
We propose in this letter a bio-inspired Quality of Service (QoS) routing algorithm that is based on the trial/error paradigm combined with a continuous adaptive function to optimize three QoS criteria: static cumulative cost path and dynamic end-to-end delay and residual bandwidth. Our proposed approach uses a model that combines a stochastic planned pre-navigation for the exploration phase and a deterministic approach for the backward phase. We adopt a unified framework of online learning to develop a cost function. We evaluated the performance of our QoS-routing algorithm and the simulation results demonstrate substantial performance improvements for networks with dynamically changing traffic.
| Original language | English |
|---|---|
| Article number | 5959254 |
| Pages (from-to) | 1016-1018 |
| Number of pages | 3 |
| Journal | IEEE Communications Letters |
| Volume | 15 |
| Issue number | 9 |
| DOIs | |
| State | Published - Sep 2011 |
Keywords
- Quality of service routing
- bio-inspired reinforcement learning
- optimization
- performance evaluation
ASJC Scopus subject areas
- Modeling and Simulation
- Computer Science Applications
- Electrical and Electronic Engineering
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