Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers


Romil Bhardwaj, Microsoft and UC Berkeley; Zhengxu Xia, University of Chicago; Ganesh Ananthanarayanan, Microsoft; Junchen Jiang, University of Chicago; Yuanchao Shu and Nikolaos Karianakis, Microsoft; Kevin Hsieh, Microsoft; Paramvir Bahl, Microsoft; Ion Stoica, UC Berkeley


Video analytics applications use edge compute servers for processing videos. Compressed models that are deployed on the edge servers for inference suffer from data drift where the live video data diverges from the training data. Continuous learning handles data drift by periodically retraining the models on new data. Our work addresses the challenge of jointly supporting inference and retraining tasks on edge servers, which requires navigating the fundamental tradeoff between the retrained model’s accuracy and the inference accuracy. Our solution Ekya balances this tradeoff across multiple models and uses a micro-profiler to identify the models most in need of retraining. Ekya’s accuracy gain compared to a baseline scheduler is 29% higher, and the baseline requires 4× more GPU resources to achieve the same accuracy as Ekya.

NSDI '22 Open Access Sponsored by
King Abdullah University of Science and Technology (KAUST)

Open Access Media

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.

@inproceedings {276952,
author = {Romil Bhardwaj and Zhengxu Xia and Ganesh Ananthanarayanan and Junchen Jiang and Yuanchao Shu and Nikolaos Karianakis and Kevin Hsieh and Paramvir Bahl and Ion Stoica},
title = {Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers},
booktitle = {19th USENIX Symposium on Networked Systems Design and Implementation (NSDI 22)},
year = {2022},
isbn = {978-1-939133-27-4},
address = {Renton, WA},
pages = {119--135},
url = {https://www.usenix.org/conference/nsdi22/presentation/bhardwaj},
publisher = {USENIX Association},
month = apr

Presentation Video