Optimizing traffic distribution in multi-radio multi-channel wireless mesh networks under dynamic traffic demand

Abstract

Wireless mesh networks have attracted increasing attention and deployment as a high-performance and low-cost solution to last-mile broadband Internet access. The capability of balancing the traffic load along different paths over different spatial regions and across difference spectrums plays a critical role in determining the performance of a wireless mesh network. To investigate the best solution, existing work proposes to formulate the mesh network load balancing problem as an optimization problem. In this problem formulation, traffic demand is usually implicitly assumed as static and known a priori. Contradictorily, recent studies of wireless network traces show that the traffic demand, even being aggregated at access points, is highly dynamic and hard to estimate. Thus, in order to apply an optimization-based solution to practice, the dynamic and volatile nature of wireless traffic demand has to be taken into account.

In this dissertation, I propose an integrated framework for wireless mesh network routing under dynamic traffic demand. This framework consists of two important components: traffic estimation and traffic distribution. To perform traffic estimation, I analyze the traffic traces collected from actual wireless networks and build time series models to characterize and predict traffic load at wireless access points. To identify the optimal traffic distribution strategies that can incorporate the uncertainty in traffic estimation and balance the traffic load, I first formulate the uncertainty-aware wireless routing problem for single-channel and single-radio mesh networks as a stochastic optimization problem and develop a fast approximation algorithm to solve it. This baseline routing problem formulation and algorithm are further extended to handle the task of traffic distribution in multi-channel and multi-radio networks via joint channel assignment and routing. Extensive simulation studies are performed at both flow level and packet level. The results show that our integrated traffic estimation and distribution solution can significantly improve the expected network performance under dynamic wireless traffic load.