Qos-aware predictive rate allocation over heterogeneous wireless interfaces

Abstract

The rapid growth of mobile data traffic is straining cellular networks. A natural approach to alleviate cellular networks congestion is to use, in addition to the cellular interface, secondary interfaces such as WiFi, Dynamic spectrum and mmWave to aid cellular networks in handling mobile traffic. The fundamental question now becomes: How should traffic be distributed over different interfaces, taking into account different application QoS requirements and the diverse nature of radio interfaces. To this end, we propose the Discounted Rate Utility Maximization (DRUM) framework with interface costs as a means to quantify application preferences in terms of throughput, delay, and cost. The flow rate allocation problem can be formulated as a convex optimization problem. However, solving this problem requires non-causal knowledge of the time-varying capacities of all radio interfaces. To this end, we propose an online predictive algorithm that exploits the predictability of wireless connectivity for a small look-ahead window w. We show that, under some mild conditions, the proposed algorithm achieves a constant competitive ratio independent of the time horizon T. Furthermore, the competitive ratio approaches 1 as the prediction window increases. We also propose another predictive algorithm based on the "Receding Horizon Control" principle from control theory that performs very well in practice. Numerical simulations serve to validate our formulation, by showing that under the DRUM framework: the more delay-tolerant the flow, the less it uses the cellular network, preferring to transmit in high rate bursts over the secondary interfaces. Conversely, delay-sensitive flows consistently transmit irrespective of different interfaces' availability. Simulations also show that the proposed online predictive algorithms have a near-optimal performance compared to the offline prescient solution under all considered scenarios.