Verifiable Network-Performance Measurements

In the current Internet, there is no clean way for affected parties to react to poor forwarding performance: when a domain violates its Service Level Agreement (SLA) with a contractual partner, the partner must resort to ad-hoc probing-based monitoring to determine the existence and extent of the violation. Instead, we propose a new, systematic approach to the problem of forwarding-performance verification. Our mechanism relies on voluntary reporting, allowing each domain to disclose its loss and delay performance to its neighbors; it does not disclose any information regarding the participating domains' topology or routing policies beyond what is already publicly available. Most importantly, it enables verifiable performance measurements, i.e., domains cannot abuse it to significantly exaggerate their performance. Finally, our mechanism is tunable, allowing each participating domain to determine how many resources to devote to it independently (i.e., without any inter-domain coordination), exposing a controllable trade-off between performance-verification quality and resource consumption. Our mechanism comes at the cost of deploying modest functionality at the participating domains' border routers; we show that it requires reasonable processing and memory resources within modern network capabilities.Comment: 14 page

Coarse-to-Fine Lifted MAP Inference in Computer Vision

There is a vast body of theoretical research on lifted inference in probabilistic graphical models (PGMs). However, few demonstrations exist where lifting is applied in conjunction with top of the line applied algorithms. We pursue the applicability of lifted inference for computer vision (CV), with the insight that a globally optimal (MAP) labeling will likely have the same label for two symmetric pixels. The success of our approach lies in efficiently handling a distinct unary potential on every node (pixel), typical of CV applications. This allows us to lift the large class of algorithms that model a CV problem via PGM inference. We propose a generic template for coarse-to-fine (C2F) inference in CV, which progressively refines an initial coarsely lifted PGM for varying quality-time trade-offs. We demonstrate the performance of C2F inference by developing lifted versions of two near state-of-the-art CV algorithms for stereo vision and interactive image segmentation. We find that, against flat algorithms, the lifted versions have a much superior anytime performance, without any loss in final solution quality.Comment: Published in IJCAI 201

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

Designing data center networks for high throughput

Data centers with tens of thousands of servers now support popular Internet services, scientific research, as well as industrial applications. The network is the foundation of such facilities, giving the large server pool the ability to work together on these applications. The network needs to provide high throughput between servers to ensure that computations are not slowed down by network bottlenecks, with servers waiting on data from other servers. This work address two broad, related questions about high-throughput data center network design: (a) how do we measure and benchmark various network designs for throughput? and (b) how do we design such networks for near-optimal throughput? The problem of designing high-throughput networks has received a lot of attention, with multiple interesting architectures being proposed every year. However, there is no clarity on how one should benchmark these networks and how they compare to each other. In fact, this work shows that commonly used measurement approaches, in particular, cut-metrics like bisection bandwidth, do not predict throughput accurately. In contrast, we directly evaluate the throughput of networks on both uniform and (heretofore unknown) nearly-worst-case traffic matrices, and include here a comparison of 10 networks using this approach. Further, prior work has not addressed a fundamental question: how far are we from throughput-optimal design? In this work, we propose the first upper bound on network throughput for any topology with identical switches. Although designing optimal topologies is infeasible, we demonstrate that random graphs achieve throughput surprisingly close to this bound -- within a few percent at the scale of a few thousand servers for uniform traffic. Our approach also addresses important practical concerns in the design of data center networks, such as incremental expansion and heterogeneous design – as more and varied equipment is added to a data center over the years in response to evolving needs, how do we best accommodate such equipment? Our networks can achieve the same incremental growth at 40% of the expense such growth would incur with past techniques for Clos networks. Further, our approach to designing heterogeneous topologies (i.e., where all the network switches are not identical) achieves 43% higher throughput than a comparable VL2 topology, a heterogeneous network already deployed in Microsoft’s data centers. We acknowledge that the use of random graphs also poses challenges, particularly with regards to efficient routing and physical cabling. We thus present here high-efficiency routing and cabling schemes for such networks as well

Performance-Driven Internet Path Selection

Internet routing can often be sub-optimal, with the chosen routes providing worse performance than other available policy-compliant routes. This stems from the lack of visibility into route performance at the network layer. While this is an old problem, we argue that recent advances in programmable hardware finally open up the possibility of performance-aware routing in a deployable, BGP-compatible manner. We introduce ROUTESCOUT, a hybrid hardware/software system supporting performance-based routing at ISP scale. In the data plane, ROUTESCOUT leverages P4-enabled hardware to monitor performance across policy-compliant route choices for each destination, at line-rate and with a small memory footprint. ROUTESCOUT's control plane then asynchronously pulls aggregated performance metrics to synthesize a performance-aware forwarding policy. We show that ROUTESCOUT can monitor performance across most of an ISP's traffic, using only 4 MB of memory. Further, its control can flexibly satisfy a variety of operator objectives, with sub-second operating times