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.
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