Multitenancy for Fast and Programmable Networks in the Cloud


Tao Wang, New York University; Hang Zhu, Johns Hopkins University; Fabian Ruffy, New York University; Xin Jin, Johns Hopkins University; Anirudh Sivaraman, New York University; Dan R. K. Ports, Microsoft Research; Aurojit Panda, New York University


Fast and programmable network devices are now readily available, both in the form of programmable switches and smart network-interface cards. Going forward, we envision that these devices will be widely deployed in the networks of cloud providers (e.g., AWS, Azure, and GCP) and exposed as a programmable surface for cloud customers—similar to how cloud customers can today rent CPUs, GPUs, FPGAs, and ML accelerators. Making this vision a reality requires us to develop a mechanism to share the resources of a programmable network device across multiple cloud tenants. In other words, we need to provide multitenancy on these devices. In this position paper, we design compile and run-time approaches to multitenancy. We present preliminary results showing that our design provides both efficient resource utilization and isolation of tenant programs from each other.

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@inproceedings {254134,
author = {Tao Wang and Hang Zhu and Fabian Ruffy and Xin Jin and Anirudh Sivaraman and Dan R. K. Ports and Aurojit Panda},
title = {Multitenancy for Fast and Programmable Networks in the Cloud},
booktitle = {12th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 20)},
year = {2020},
url = {},
publisher = {USENIX Association},
month = jul

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