Manufacturing, automotive, and aerospace environments use embedded systems for control and automation and need to fulfill strict real-time guarantees. To facilitate more efficient business processes and remote control, such devices are being connected to IP networks. Due to the difficulty in predicting network packets and the interrelated workloads of interrupt handlers and drivers, devices controlling time critical processes stand under the risk of missing process deadlines when under high network loads. Additionally, devices at the edge of large networks and the internet are subject to a high risk of load spikes and network packet overloads. In this paper, we investigate strategies to detect network packet overloads in real-time and present four approaches to adaptively mitigate local deadline misses. In addition to two strategies mitigating network bursts with and without hysteresis, we present and discuss two novel mitigation algorithms, called Budget and Queue Mitigation. In an experimental evaluation, all algorithms showed mitigating effects, with the Queue Mitigation strategy enabling most packet processing while preventing lateness of critical tasks.

中文翻译：

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检测和缓解IoT系统中实时设备上的网络数据包过载

制造，汽车和航空航天环境使用嵌入式系统进行控制和自动化，并且需要实现严格的实时保证。为了促进更有效的业务流程和远程控制，此类设备已连接到IP网络。由于难以预测网络数据包以及中断处理程序和驱动程序的相关工作量，因此，在网络负载较高的情况下，控制时间紧迫的进程的设备可能会丢失进程截止日期。此外，大型网络和Internet边缘的设备承受负载峰值和网络数据包过载的高风险。在本文中，我们研究了实时检测网络数据包过载的策略，并提出了四种方法来自适应地缓解本地期限丢失。除了有缓解和无迟滞的两种网络缓解策略外，我们还介绍和讨论两种新颖的缓解算法，称为预算和队列缓解。在实验评估中，所有算法均显示出缓解效果，而队列缓解策略可实现大多数数据包处理，同时又可避免关键任务的延迟。