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Embedded and real-time devices in many domains are increasingly dependent on network connectivity. The ability to offload computations encourages Cost, Size, Weight and Power (C-SWaP) optimizations, while coordination over the network effectively enables systems to sense the environment beyond their own local sensors, and to collaborate globally. The promise is significant: Autonomous Vehicles (AVs) coordinating with each other through infrastructure, factories aggregating data for global optimization, and power-constrained devices leveraging offloaded inference tasks. Low-latency wireless (e.g., 5G) technologies paired with the edge cloud, are further enabling these trends. Unfortunately, computation at the edge poses significant challenges due to the challenging combination of limited resources, required high performance, security due to multi-tenancy, and real-time latency. This paper introduces Edge-RT, a set of OS extensions for the edge designed to meet the end-to-end (packet reception to transmission) deadlines across chains of computations. It supports strong security by executing a chain per-client device, thus isolating tenant and device computations. Despite a practical focus on deadlines and strong isolation, it maintains high system efficiency. To do so, Edge-RT focuses on per-packet deadlines inherited by the computations that operate on it. It introduces mechanisms to avoid per-packet system overheads, while trading only bounded impacts on predictable scheduling. Results show that compared to Linux and EdgeOS, Edge-RT can both maintain higher throughput and meet significantly more deadlines both for systems with bimodal workloads with utilization above 60%, in the presence of malicious tasks, and as the system scales up in clients. 

5G edge clouds promise a pervasive computational infrastructure a short network hop away, enabling a new breed of smart devices that respond in real-time to their physical surroundings. Unfortunately, today’s operating system designs fail to meet the goals of scalable isolation, dense multi-tenancy, and high performance needed for such applications. In this paper we introduce EdgeOS that emphasizes system-wide isolation as fine-grained as per-client. We propose a novel memory movement accelerator architecture that employs data copying to enforce strong isolation without performance penalties. To support scalable isolation, we introduce a new protection domain implementation that offers lightweight isolation, fast startup and low latency even under high churn. We implement EdgeOS in a microkernel based OS and demonstrate running high scale network middleboxes using the Click software router and endpoint applications such as memcached, a TLS proxy, and neural network inference. We reduce startup latency by 170X compared to Linux processes, and improve latency by three orders of magnitude when running 300 to 1000 edge-cloud memcached instances on one server.