Data center networks (DCNs) form the backbone infrastructure of many large-scale enterprise applications as well as emerging cloud computing providers. This paper describes the design, implementation and evaluation of OSA, a novel Optical Switching Architecture for DCNs. Leveraging runtime reconfigurable optical devices, OSA dynamically changes its topology and link capacities, thereby achieving unprecedented flexibility to adapt to dynamic traffic patterns. Extensive analytical simulations using both real and synthetic traffic patterns demonstrate that OSA can deliver high bisection bandwidth (60%-100% of the non-blocking architecture). Implementation and evaluation of a small-scale functional prototype further demonstrate the feasibility of OSA.

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For many Internet services, reducing latency improves the user experience and increases revenue for the service provider. While in principle latencies could nearly match the speed of light, we find that infrastructural inefficiencies and protocol overheads cause today's Internet to be much slower than this bound: typically by more than one, and often, by more than two orders of magnitude. Bridging this large gap would not only add value to today's Internet applications, but could also open the door to exciting new applications. Thus, we propose a grand challenge for the networking research community: a speed-of-light Internet. To inform this research agenda, we investigate the causes of latency inflation in the Internet across the network stack. We also discuss a few broad avenues for latency improvement.

Abstract-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.