Today's large content providers (CP) are busy building out their service infrastructures or "peering edges" to satisfy the insatiable demand for content created by an ever-expanding Internet edge. One component of these serving infrastructures that features prominently in this build-out is their connectivity fabric; i.e., the set of all Internet interconnections that content has to traverse en route from the CP's various "deployments" or "serving sites" to end users. However, these connectivity fabrics have received little attention in the past and remain largely ill-understood.

For many years, the research community, practitioners, and regulators have used myriad methods and tools to understand the complex structure and behavior of ISPs from the edge of the network. Unfortunately, the nature of these techniques forces the researcher to find a balance between ISP-coverage, user scale, and accuracy. In this paper we present AdTag, a network measurement paradigm that leverages the opportunistic nature of online targeted advertising to measure the Internet from the edge of the network. We discuss and formalize AdTag's design space---including technical, ethical, deployability and economic factors---and its potential to analyze a wide spectrum of Internet connectivity aspects from the browser. We run several experiments to demonstrate that AdTag can be tailored towards geographic and device-based user groups, finding also several challenges to be faced in order to maximize the number of samples. In a 7-day campaign, AdTag could access more than 20K ISPs at a global scale (185 countries) using millions of edge nodes.

Network stacks currently implemented in operating systems can no longer cope with the packet rates offered by 10 Gbit Ethernet. Thus, frameworks were developed claiming to offer a faster alternative for this demand. These frameworks enable arbitrary packet processing systems to be built from commodity hardware handling a traffic rate of several 10 Gbit interfaces, entering a domain previously only available to custom-built hardware. In this paper, we survey various frameworks for high-performance packet IO. We analyze the performance of the most prominent frameworks based on representative measurements in packet forwarding scenarios. Therefore, we quantify the effects of caching and look at the tradeoff between throughput and latency. Moreover, we introduce a model to estimate and assess the performance of these packet processing frameworks.

We present MoonGen, a flexible high-speed packet generator. It can saturate 10 GbE links with minimum-sized packets while using only a single CPU core by running on top of the packet processing framework DPDK. Linear multi-core scaling allows for even higher rates: We have tested MoonGen with up to 178.5 Mpps at 120 Gbit/s. Moving the whole packet generation logic into user-controlled Lua scripts allows us to achieve the highest possible flexibility. In addition, we utilize hardware features of commodity NICs that have not been used for packet generators previously. A key feature is the measurement of latency with sub-microsecond precision and accuracy by using hardware timestamping capabilities of modern commodity NICs. We address timing issues with software-based packet generators and apply methods to mitigate them with both hardware support and with a novel method to control the inter-packet gap in software. Features that were previously only possible with hardware-based solutions are now provided by MoonGen on commodity hardware. MoonGen is available as free software under the MIT license in our git repository at https://github.com/emmericp/MoonGen