Fine-Grained Isolation for Scalable, Dynamic, Multi-tenant Edge Clouds


Yuxin Ren, The George Washington University; Guyue Liu, Carnegie Mellon University; Vlad Nitu, INSA Lyon France; Wenyuan Shao, Riley Kennedy, Gabriel Parmer, and Timothy Wood, The George Washington University; Alain Tchana, ENS Lyon France


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.

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@inproceedings {254426,
author = {Yuxin Ren and Guyue Liu and Vlad Nitu and Wenyuan Shao and Riley Kennedy and Gabriel Parmer and Timothy Wood and Alain Tchana},
title = {Fine-Grained Isolation for Scalable, Dynamic, Multi-tenant Edge Clouds},
booktitle = {2020 {USENIX} Annual Technical Conference ({USENIX} {ATC} 20)},
year = {2020},
isbn = {978-1-939133-14-4},
pages = {927--942},
url = {},
publisher = {{USENIX} Association},
month = jul,

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