Predictable Performance Optimization for Wireless Networks

We present a novel approach to optimize the performance of IEEE 802.11-based multi-hop wireless networks. A unique feature of our approach is that it enables an accurate prediction of the resulting throughput of individual flows. At its heart lies a simple yet realistic model of the network that captures interference, traffic, and MAC-induced dependencies. Unless properly accounted for, these dependencies lead to unpredictable behaviors. For instance, we show that even a simple network of two links with one flow is vulnerable to severe performance degradation. We design algorithms that build on this model to optimize the network for fairness and throughput. Given traffic demands as input, these algorithms compute rates at which individual flows must send to meet the objective. Evaluation using a multi-hop wireless testbed as well as simulations show that our approach is very effective. When optimizing for fairness, our methods result in close to perfect fairness. When optimizing for throughput, they lead to 100-200% improvement for UDP traffic and 10-50% for TCP traffic.