FaaSLearner: Resource-Efficient Edge Video Analytics via Correlation-Aware Multi-Model Continual Learning

Abstract

Video analytics services utilizing multiple deep neural network models (DNNs) are increasingly being adopted in various edge intelligence applications. To avoid the accuracy reduction caused by data drift, existing works leverage continual learning that directly retrains DNN models in multi-model applications. According to our preliminary experiments, we observe that existing continual learning frameworks neglect the accuracy correlation among multiple DNN models and the opportunity to harvest idle resources during model retraining, leading to considerable resource inefficiency. In this paper, we propose FaaSLearner, an algorithm-system co-designed continual learning framework for multi-model video analytics that leverages the agile and fine-grained resource management of serverless computing. Specifically, FaaSLearner proposes the correlation-aware retraining planning to analyze the multi-model correlation, and then selectively retrain DNN models with the greatest in accuracy gain. In addition, FaaSLearner proposes the resource-efficient retraining scheduling to accurately trigger the continual learning, and then harvest the keep-alive periods for retraining tasks without interfering with normal inference. We evaluate FaaSLearner with four common edge video analytics applications with the Azure public dataset. Extensive experiments show that, FaaSLearner improves the average application accuracy up to 31.9% over representative baselines, and harvests more than 2.51 × idle memory resources for retraining.