Profile-driven regression for modeling and runtime optimization of mobile networks

Computer networks often display nonlinear behavior when examined over a wide range of operating conditions. There are few strategies available for modeling such behavior and optimizing such systems as they run. Profile-driven regression is developed and applied to modeling and runtime optimization of throughput in a mobile ad hoc network, a self-organizing collection of mobile wireless nodes without any fixed infrastructure. The intermediate models generated in profile-driven regression are used to fit an overall model of throughput, and are also used to optimize controllable factors at runtime. Unlike others, the throughput model accounts for node speed. The resulting optimization is very effective; locally optimizing the network factors at runtime results in throughput as much as six times higher than that achieved with the factors at their default levels.