Opportunistic traffic scheduling over multiple network paths

Multipath routing enables a network's traffic to be split among two or more possibly disjoint paths in order to reduce latency, improve throughput, and balance traffic loads. Yet, once the control plane establishes multiple routes, a policy is needed for efficiently splitting traffic among the selected paths. In this paper, we introduce opportunistic multipath scheduling (OMS), a technique for exploiting short term variations in path quality to minimize delay, while simultaneously ensuring that the splitting rules dictated by the routing protocol are satisfied. In particular, OMS uses measured path conditions on time scales of up to several seconds to opportunistically favor low-latency high-throughput paths. Consequently, OMS ensures that over longer time scales relevant for traffic management policies, traffic is split according to the ratios determined by the routing protocol. We develop a model of OMS and derive an asymptotic lower bound on the performance of OMS as a function of path conditions (mean, variance, and Hurst parameter) for self-similar traffic. An example finding from the model is that long-time-scale traffic fluctuations represented by a larger Hurst parameter improve the performance gain of OMS vs. round-robin scheduling, even under paths that are statistically identical. Finally, we use an extensive simulation-based performance study to evaluate the accuracy of the analytical model, explore the impact of OMS on TCP throughput, and study the impact of factors such as delayed measurements