When can we reason about the neutrality of a network based on external observations? We prove conditions under which it is possible to (a) detect neutrality violations and (b) localize them to specific links, based on external observations. Our insight is that, when we make external observations from different vantage points, these will most likely be inconsistent with each other if the network is not neutral. Where existing tomographic techniques try to form solvable systems of equations to infer network properties, we try to form unsolvable systems that reveal neutrality violations. We present an algorithm that relies on this idea to identify sets of nonneutral links based on external observations, and we show, through network emulation, that it achieves good accuracy for a variety of network conditions.

Datacenter-networking research requires tools to both generate traffic and accurately measure latency and throughput. While hardwarebased tools have long existed commercially, they are primarily used to validate ASICs and lack flexibility, e.g., to study new protocols. They are also too expensive for academics. The recent development of kernel-bypass networking and advanced NIC features such as hardware timestamping have created new opportunities for accurate latency measurements. This paper compares these two approaches, and in particular whether commodity servers and NICs, when properly configured, can measure the latency distributions as precisely as specialized hardware.Our work shows that well-designed commodity solutions can capture subtle differences in the tail latency of stateless UDP traffic. We use hardware devices as the ground truth, both to measure latency and to forward traffic. We compare the ground truth with observations that combine five latency-measuring clients and five different port forwarding solutions and configurations. State-of-theart software such as MoonGen that uses NIC hardware timestamping provides sufficient visibility into tail latencies to study the effect of subtle operating system configuration changes. We also observe that the kernel-bypass-based TRex software, that only relies on the CPU to timestamp traffic, can also provide solid results when NIC timestamps are not available for a particular protocol or device.

Is it possible to design a packet-sampling algorithm that prevents the network node that performs the sampling from treating the sampled packets preferentially? We study this problem in the context of designing a "network transparency" system. In this system, networks emit receipts for a small sample of the packets they observe, and a monitor collects these receipts to estimate each network's loss and delay performance. Sampling is a good building block for this system, because it enables a solution that is flexible and combines low resource cost with quantifiable accuracy. The challenge is cheating resistance: when a network's performance is assessed based on the conditions experienced by a small traffic sample, the network has a strong incentive to treat the sampled packets better than the rest. We contribute a sampling algorithm that is provably robust to such prioritization attacks, enables network performance estimation with quantifiable accuracy, and requires minimal resources. We confirm our analysis using real traffic traces.