Allocating resources for multimedia traffic flows with real-time performance requirements is an important challenge for future packet networks. However, in large-scale networks, individually managing each traffic flow on each of its traversed routers has fundamental scalability limitations, in both the control plane's requirements for signaling, state management, and admission control, and the data plane's requirements for per-flow scheduling mechanisms. In this paper, we develop a scalable architecture and algorithm for quality-of-service management termed egress admission control. In our approach, resource management and admission control are performed only at egress routers, without any coordination among backbone nodes or per-flow management. Our key technique is to develop a framework for admission control under a general "black box" model, which allows for cross traffic that cannot be directly measured, and scheduling policies that may be ill-described across many network nodes. By monitoring and controlling egress routers' class-based arrival and service envelopes, we show how network services can be provisioned via scalable control at the network edge. We illustrate the performance of our approach with a set of simulation experiments using highly bursty traffic flows and find that despite our use of distributed admission control, our approach is able to accurately control the system's admissible region under a wide range of conditions.

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Abstract-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. However, a naive policy that always selects the highest quality path would violate the routing protocol's path weights and potentially lead to oscillation. 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.

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