Kavli Affiliate: Ran Wang
| First 5 Authors: Guanyu Gao, Yuqi Dong, Ran Wang, ,
| Summary:
Deep Neural Network (DNN) based video analytics empowers many computer
vision-based applications to achieve high recognition accuracy. To reduce
inference delay and bandwidth cost for video analytics, the DNN models can be
deployed on the edge nodes, which are proximal to end users. However, the
processing capacity of an edge node is limited, potentially incurring
substantial delay if the inference requests on an edge node is overloaded.
While efforts have been made to enhance video analytics by optimizing the
configurations on a single edge node, we observe that multiple edge nodes can
work collaboratively by utilizing the idle resources on each other to improve
the overall processing capacity and resource utilization. To this end, we
propose a Multiagent Reinforcement Learning (MARL) based approach, named as
EdgeVision, for collaborative video analytics on distributed edges. The edge
nodes can jointly learn the optimal policies for video preprocessing, model
selection, and request dispatching by collaborating with each other to minimize
the overall cost. We design an actor-critic-based MARL algorithm with an
attention mechanism to learn the optimal policies. We build a multi-edge-node
testbed and conduct experiments with real-world datasets to evaluate the
performance of our method. The experimental results show our method can improve
the overall rewards by 33.6%-86.4% compared with the most competitive baseline
methods.
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