Placement of DNN Models on Mobile Edge Devices for Effective Video Analysis

The pervasive deployment of IoT devices along with the advancements in Deep Neural Network (DNN) models have enabled video analytics at the edge, the so-called Edge AI systems, in support of various large smart-city applications such as automatic road damage evaluation and fire detection. Current solutions require the model developer to make the placement decision by manually assigning models to edge devices. However, an Edge AI solution could entail hundreds of mobile edge devices operating in a large geographical region (e.g., installed on vehicles) with various resource capabilities and different DNN models, hence rendering manual placement ineffective. This paper presents alternative methods to automatically place various models on a diverse set of edge devices, considering the geospatial coverage of video data, resource capabilities of edge devices, and the characteristics of the trained models. First, we mathematically formulate the model placement as an optimization problem which is proven to be NP-Hard. We then propose several heuristics to solve it efficiently and evaluate them with a real-world dataset collected along the 165 bus route trajectories in the City of San Francisco. Our placement algorithm yields a higher recall in object detection and is more robust to the uncertainty of the underlying location context, without sacrificing much utilization cost.