Edge- rt:操作系统支持多租户控制延迟,实时边缘

Wenyuan Shao, Bite Ye, Huachuan Wang, Gabriel Parmer, Yuxin Ren
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引用次数: 1

摘要

许多领域的嵌入式和实时设备越来越依赖于网络连接。卸载计算的能力促进了成本、尺寸、重量和功耗(C-SWaP)的优化,而网络上的协调有效地使系统能够超越自己的本地传感器感知环境,并实现全球协作。前景非常可观:自动驾驶汽车(AVs)通过基础设施相互协调,工厂聚合数据以实现全局优化,以及利用卸载推理任务的功率受限设备。低延迟无线(例如5G)技术与边缘云相结合,进一步推动了这些趋势。不幸的是,由于有限的资源、所需的高性能、多租户带来的安全性和实时延迟的挑战性组合,边缘计算带来了重大挑战。本文介绍了edge - rt,这是一组用于边缘的操作系统扩展,旨在满足跨计算链的端到端(数据包接收到传输)截止日期。它通过对每个客户端设备执行链来支持强大的安全性,从而隔离租户和设备计算。尽管注重最后期限和强隔离,但它保持了很高的系统效率。为此,Edge-RT将重点放在每个包的截止日期上,这些截止日期由对其进行操作的计算继承。它引入了避免每包系统开销的机制,同时只对可预测调度产生有限的影响。结果表明,与Linux和EdgeOS相比,对于利用率超过60%的双峰工作负载系统、存在恶意任务以及系统在客户端中扩展时,Edge-RT既可以保持更高的吞吐量,又可以满足更多的截止日期。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Edge-RT: OS Support for Controlled Latency in the Multi-Tenant, Real-Time Edge
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.
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