Daniel Raumer | Search arXiv

arXiv2014

MoonGen: A Scriptable High-Speed Packet Generator

We present MoonGen, a flexible high-speed packet generator. It can saturate 10 GbE links with minimum sized packets 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. We move the whole packet generation logic into user-controlled Lua scripts to achieve the highest possible flexibility. In addition, we utilize hardware features of Intel 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 on commodity NICs 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 at https://github.com/emmericp/MoonGen

cs.NI↗

arXiv2014

Reaching Consensus Among Mobile Agents: A Distributed Protocol for the Detection of Social Situations

Physical social encounters are governed by a set of socio-psychological behavioral rules with a high degree of uniform validity. Past research has shown how these rules or the resulting properties of the encounters (e.g. the geometry of interaction) can be used for algorithmic detection of social interaction. In this paper, we present a distributed protocol to gain a common understanding of the existing social situations among agents. Our approach allows a group of agents to combine their subjective assessment of an ongoing social situation. Based on perceived social cues obtained from raw data signals, they reach a consensus about the existence, parameters, and participants of a social situation. We evaluate our protocol using two real-world datasets with social interaction information and additional synthetic data generated by our social-aware mobility model.

cs.SI↗