Takeaways from an experimental evaluation of eXpress Data Path (XDP) and Data Plane Development Kit (DPDK) under a Cloud Computing environment

• Autor(a) principal: Freitas, Eduardo
• Data de Publicação: 2022
• Outros Autores: Oliveira Filho, Assis T. de, Carmo, Pedro R. X. do, Sadok, Djamel F. H., Kelner, Judith
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Research, Society and Development
• Texto Completo: https://rsdjournal.org/index.php/rsd/article/view/34200
• topic: XDP; DPDK; Kernel Linux; Frameworks de Procesamiento de Paquetes; Computación en la nube.; XDP; DPDK; Kernel Linux; Frameworks de Processamento de Pacotes; Computação em nuvem.; XDP; DPDK; Linux Kernel; Fast Packet Processing Frameworks; Cloud computing.

The popularization of the Linux kernel at data center servers became a significant factor to consider when developing or deploying network applications. New “packet processing frameworks” emerged to ensure fast packet processing inside these servers that rely on the Linux kernel, XDP and DPDK being the two main ones. Alongside this, Cloud Computing environments dominated the data center scenario, bringing virtualization to the center of these servers. This research compares DPDK and XDP packet processors when subject to a cloud computing environment with I/O, CPU, and network load. We first describe an architecture that implements frameworks and emulates a cloud environment. We measure throughput and packet loss while varying the number of competing VMs, CPU usage by the frameworks, and packet size and present our results in the form of takeaways. Furthermore, we conclude that the virtual environment can impact the performance of both frameworks depending on the received packet size and the type of workload the cloud environment performs. A significant case to highlight is when the cloud environment performs network load, and the servers receive small-sized packets, creating both throughput degradation and increasing packet loss. We conclude our experiments by conducting statistical non-parametric hypothesis tests to enhance our conclusions and have a closer look at the results.