Priority-aware Inter-Server Receive Side Scaling

F. Biersack, Kilian Holzinger, Henning Stubbe, Thomas Wild, G. Carle, A. Herkersdorf
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Abstract

Next-generation automotive networks will be characterized by a high number of interconnected sensors, actuators and applications on electronic control units communicating with each other over a high-speed Ethernet backbone network. As these applications have various criticalities, high volumes of fluctuating traffic with different priorities will have to be processed in a reliable and efficient manner. To cope with these challenges, we present Priority-aware Inter-Server Receive Side Scaling (prioRSS), a new SmartNIC-based hardware accelerator designed for automotive compute nodes. prioRSS builds upon Receive Side Scaling and introduces priority-awareness into an intra- and inter-node load balancer. It uses a priority-partitioned indirection table within which flows of the same priority are bundled. Low-latency reconfigurations issued by a Network Health Monitoring software allow for adapting the table content to changing network conditions. Simulative evaluations and comparisons to a priority-unaware version of our design show that prioRSS enables per-priority resource assignments without degrading end-to-end packet latencies while using the same table memory space. Paired with a priority-aware scheduler, end-to-end latencies of high priority flows can be notably reduced compared to average packet latencies, at the expense of lowest priority traffic. The best results are acquired when partitioning the table proportionally to the associated traffic share.
优先级感知的服务器间接收端扩展
下一代汽车网络的特点是大量相互连接的传感器、执行器和电子控制单元上的应用程序通过高速以太网骨干网相互通信。由于这些应用程序具有各种关键性,因此必须以可靠和有效的方式处理具有不同优先级的大量波动流量。为了应对这些挑战,我们提出了优先级感知的服务器间接收端缩放(prioRSS),这是一种新的基于智能网卡的硬件加速器,专为汽车计算节点设计。prioRSS建立在接收端缩放的基础上,并在节点内和节点间负载平衡器中引入了优先级感知。它使用优先级分区的间接表,在其中捆绑了具有相同优先级的流。由网络运行状况监视软件发布的低延迟重新配置允许调整表内容以适应不断变化的网络条件。模拟评估和与我们设计的不知道优先级的版本的比较表明,prioRSS在使用相同的表内存空间时支持按优先级分配资源,而不会降低端到端数据包延迟。与优先级感知调度器配合使用,与平均数据包延迟相比,高优先级流的端到端延迟可以显著降低,但代价是降低最低优先级的流量。根据关联的流量共享按比例对表进行分区,可以获得最佳结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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