Consistency-Aware Weather Disruption-Tolerant Routing in SDN-Based Wireless Mesh Networks

Wireless network solutions, a dominant enabling technology for the backhaul segment, are susceptible to weather disturbances that can substantially degrade network throughput and/or delay, compromising the stringent 5G requirements. These effects can be alleviated by centralized rerouting realized by software defined networking architecture. However, careless frequent reconfigurations can lead to inconsistencies in the network states due to asynchrony between different switches, which can create congestion and limit the rerouting gain. The aim of this paper is to minimize the total data loss during rain disturbance by proposing an algorithm that decides on the timing, the sequence, and the paths for rerouting of network flows considering the imposed congestion during reconfiguration. At each time sample, the central controller decides whether to adopt the optimal routes at a switching cost, defined as the imposed congestion, or to keep using existing, sub-optimal routes at a throughput loss. To find optimal solutions with minimal data loss in a static scenario, we formulate a dynamic programming problem that utilizes perfect knowledge of rain attenuation for the whole rain period. For dynamic scenarios with unknown future rain attenuation, we propose an online consistency-aware rerouting algorithm, called consistency-aware rerouting with prediction (CARP), which uses the temporal correlation of rain fading to estimate future rain attenuation. Simulation results on synthetic and real networks validate the efficiency of our CARP algorithm, substantially reducing data loss and increasing network throughput with a fewer number of rerouting actions compared to a greedy and a regular rerouting benchmarking approaches.