Introduction to queueing networks
Introduction to queueing networks
Stability of the random neural network model
Neural Computation
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Learning in the recurrent random neural network
Neural Computation
Routing in the Internet
Diffusion based statistical call admission control in ATM
Performance Evaluation
Measurement and performance of a cognitive packet network
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on networking middleware: selected papers from the TERENA networking conference 2001
Design and performance of cognitive packet networks
Performance Evaluation
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Networking with Cognitive Packets
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Networks with Cognitive Packets
MASCOTS '00 Proceedings of the 8th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems
Self-Awareness and Adaptivity for Quality of Service
ISCC '03 Proceedings of the Eighth IEEE International Symposium on Computers and Communications
Cognitive Packet Networks: QoS and Performance
MASCOTS '02 Proceedings of the 10th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
Putting more genetics into genetic algorithms
Evolutionary Computation
Block loss reduction in ATM networks
Computer Communications
IEEE Network: The Magazine of Global Internetworking
| Hi-index | 0.00 |
Source routing of packets in the Internet requires that a path be selected in advance and stored at the source nodes. Path selection is typically based on Quality of Service (QoS) criteria like packet delay, delay jitter, and loss. A new protocol called the "Cognitive Packet Network" (CPN) [18, 19, 20, 21] has been proposed which is capable of dynamically choosing paths through a store and forward packet switching network like the Internet so as to provide best effort QoS to peer-to-peer connections. A CPN-enabled network uses smart packets to discover routes based on QoS requirements; acknowledgement (ACK) packets to deliver the routes back to source nodes; dumb packets to carry user-payload; and reinforcement learning to conduct path selection. We extended the path discovery process in CPN by introducing a genetic algorithm (GA) that can help discover new paths that may not have been discovered by smart packets [28]. In this paper, we further extend CPN with GA by prioritizing paths discovered based on their ages, adopting a progressive fitness evaluation system, and introducing a new genetic operator - mutation. The simulation topology has also been upgraded from a 10 by 10 grid to an arbitrarily connected network. We detail the design of the algorithms and their implementations, and finally report on resulting QoS measurements.