Characterization and Prediction of Performance Interference on Mediated Passthrough GPUs for Interference-aware Scheduler

Authors: 

Xin Xu, Na Zhang, and Michael Cui, VMware Inc; Michael He, The University of Texas at Austin; Ridhi Surana, VMware Inc

Abstract: 

Sharing GPUs in the cloud is cost effective and can facilitate the adoption of hardware accelerator enabled cloud. Butsharing causes interference between co-located VMs andleads to performance degradation. In this paper, we proposedan interference-aware VM scheduler at the cluster level withthe goal of minimizing interference. NVIDIA vGPU pro-vides sharing capability and high performance, but it has unique performance characteristics, which have not been studied thoroughly before. Our study reveals several key ob-servations. We leverage our observations to construct modelsbased on machine learning techniques to predict interferencebetween co-located VMs on the same GPU. We proposed a system architecture leveraging our models to schedule VMs to minimize the interference. The experiments show that our observations improves the model accuracy (by 15% ̃ 40%) and the scheduler reduces application run-time overhead by 24.2% in simulated scenarios.

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BibTeX
@inproceedings {234855,
author = {Xin Xu and Na Zhang and Michael Cui and Michael He and Ridhi Surana},
title = {Characterization and Prediction of Performance Interference on Mediated Passthrough GPUs for Interference-aware Scheduler},
booktitle = {11th {USENIX} Workshop on Hot Topics in Cloud Computing (HotCloud 19)},
year = {2019},
address = {Renton, WA},
url = {https://www.usenix.org/conference/hotcloud19/presentation/xu-xin},
publisher = {{USENIX} Association},
month = jul,
}