Oscillations in a bio-inspired routing algorithm

Adaptive routing is once again becoming of interest because of the possibility to couple on-line probing in networks with real-time dynamic and distributed control of paths and flows using Reinforcement Learning. Wireless networks, with their rapidly changing network conditions also create a need to revisit this issue. This paper uses measurements in a wired, bio-inspired adaptive network testbed, the Cognitive Packet Network (CPN), to investigate the pros and cons of adaptive routing. CPN routes packet flows through a store and forward network according to their Quality of Service (QoS) needs through an on-line, distributed reinforcement learning mechanism that incorporates a biologically-inspired Neural Network model for making routing decisions. This paper investigates routing oscillations which occur due to the interaction of multiple flows and studies their effect on QoS in the context of CPN. Our results indicate that routing oscillations can be easily controlled by randomising the route switching, and that from an overall QoS viewpoint increased switching can also lead to improved performance.