Kavli Affiliate: Ran Wang
| First 5 Authors: Guanyu Gao, Yuqi Dong, Ran Wang, Xin Zhou,
| Summary:
Deep Neural Network (DNN)-based video analytics significantly improves
recognition accuracy in computer vision applications. Deploying DNN models at
edge nodes, closer to end users, reduces inference delay and minimizes
bandwidth costs. However, these resource-constrained edge nodes may experience
substantial delays under heavy workloads, leading to imbalanced workload
distribution. While previous efforts focused on optimizing hierarchical
device-edge-cloud architectures or centralized clusters for video analytics, we
propose addressing these challenges through collaborative distributed and
autonomous edge nodes. Despite the intricate control involved, we introduce
EdgeVision, a Multiagent Reinforcement Learning (MARL)- based framework for
collaborative video analytics on distributed edges. EdgeVision enables edge
nodes to autonomously learn policies for video preprocessing, model selection,
and request dispatching. Our approach utilizes an actor-critic-based MARL
algorithm enhanced with an attention mechanism to learn optimal policies. To
validate EdgeVision, we construct a multi-edge testbed and conduct experiments
with real-world datasets. Results demonstrate a performance enhancement of
33.6% to 86.4% compared to baseline methods.
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