Symptotics: A framework for analyzing the scalability of real-world wireless networks

We present a framework for non-asymptotic analysis of real-world wireless networks that captures protocol overhead, congestion bottlenecks, traffic heterogeneity and other real-world concerns. The framework introduces the definition of symptotic 1 scalability, and a metric called change impact value (CIV) for comparing the impact of underlying system parameters on network scalability. A key idea is to divide analysis into generic and specific parts connected via a signature - a set of governing parameters of a network scenario - such that analyzing a new network scenario reduces mainly to identifying its signature. Using this framework, we present approximate scalability expressions for line, mesh and clique topologies using TDMA and 802.11, for unicast and broadcast traffic. We compare the analysis with discrete event simulations and show that the model provides sufficiently accurate estimates of scalability. Based on the symptotic expressions, we study the change impact value of underlying parameters on network scalability. We show how impact analysis can be used to tune network features to meet a scaling requirement, and determine the regimes in which reducing routing overhead impacts performance.