Lying Your Way to Better Traffic Engineering

To optimize the flow of traffic in IP networks, operators do traffic engineering (TE), i.e., tune routing-protocol parameters in response to traffic demands. TE in IP networks typically involves configuring static link weights and splitting traffic between the resulting shortest-paths via the Equal-Cost-MultiPath (ECMP) mechanism. Unfortunately, ECMP is a notoriously cumbersome and indirect means for optimizing traffic flow, often leading to poor network performance. Also, obtaining accurate knowledge of traffic demands as the input to TE is elusive, and traffic conditions can be highly variable, further complicating TE. We leverage recently proposed schemes for increasing ECMP's expressiveness via carefully disseminated bogus information ("lies") to design COYOTE, a readily deployable TE scheme for robust and efficient network utilization. COYOTE leverages new algorithmic ideas to configure (static) traffic splitting ratios that are optimized with respect to all (even adversarially chosen) traffic scenarios within the operator's "uncertainty bounds". Our experimental analyses show that COYOTE significantly outperforms today's prevalent TE schemes in a manner that is robust to traffic uncertainty and variation. We discuss experiments with a prototype implementation of COYOTE

Measuring and Understanding Throughput of Network Topologies

High throughput is of particular interest in data center and HPC networks. Although myriad network topologies have been proposed, a broad head-to-head comparison across topologies and across traffic patterns is absent, and the right way to compare worst-case throughput performance is a subtle problem. In this paper, we develop a framework to benchmark the throughput of network topologies, using a two-pronged approach. First, we study performance on a variety of synthetic and experimentally-measured traffic matrices (TMs). Second, we show how to measure worst-case throughput by generating a near-worst-case TM for any given topology. We apply the framework to study the performance of these TMs in a wide range of network topologies, revealing insights into the performance of topologies with scaling, robustness of performance across TMs, and the effect of scattered workload placement. Our evaluation code is freely available

Improving Oblivious Reconfigurable Networks with High Probability

Oblivious Reconfigurable Networks (ORNs) use rapidly reconfiguring switches to create a dynamic time-varying topology. Prior theoretical work on ORNs has focused on the tradeoff between maximum latency and guaranteed throughput. This work shows that by relaxing the notion of guaranteed throughput to an achievable rate with high probability, one can achieve a significant improvement in the latency/throughput tradeoff. For a fixed maximum latency, we show that almost twice the maximum possible guaranteed throughput rate can be achieved with high probability. Alternatively for a fixed throughput value, relaxing to achievement with high probability decreases the maximum latency to almost the square root of the latency required to guarantee the throughput rate. We first give a lower bound on the best maximum latency possible given an achieved throughput rate with high probability. This is done using an LP duality style argument. We then give a family of ORN designs which achieves these tradeoffs. The connection schedule is based on the Vandermonde Basis Scheme of Amir, Wilson, Shrivastav, Weatherspoon, Kleinberg, and Agarwal, although the period and routing scheme differ significantly. We prove achievable throughput with high probability by interpreting the amount of flow on each edge as a sum of negatively associated variables, and applying a Chernoff bound. This gives us a design with maximum latency that is tight with our lower bound (up to a log factor) for almost all constant throughput values.Comment: 19 pages, 1 figur