Techniques for fast and scalable time series traffic generation

Many IoT applications ingest and process time series data with emphasis on 5Vs (Volume, Velocity, Variety, Value and Veracity). To design and test such systems, it is desirable to have a high-performance traffic generator specifically designed for time series data, preferably using archived data to create a truly realistic workload. However, most existing traffic generator tools either are designed for generic network applications, or only produce synthetic data based on certain time series models. In addition, few have raised their performance bar to millions-packets-per-second level with minimum time violations. In this paper, we design, implement and evaluate a highly efficient and scalable time series traffic generator for IoT applications. Our traffic generator stands out in the following four aspects: 1) it generates time-conforming packets based on high-fidelity reproduction of archived time series data; 2) it leverages an open-source Linux Exokernel middleware and a customized userspace network subsystem; 3) it includes a scalable 10G network card driver and uses \"absolute\" zero-copy in stack processing; and 4) it has an efficient and scalable application-level software architecture and threading model. We have conducted extensive experiments on both a quad-core Intel workstation and a 20-core Intel server equipped with Intel X540 10G network cards and Samsung's NVMe SSDs. Compared with a stock Linux baseline and a traditional mmap-based file I/O approach, we observe that our traffic generator significantly outperforms other alternatives in terms of throughput (10X), scalability (3.6X) and time violations (46.2X).