The accelerated integration of edge computing and artificial intelligence has promoted the rise of edge intelligence, which is regarded as the key to solving the last-mile delivery problem of artificial intelligence technology. Although previous research has made many efforts in this area, they have struggled to serve the scenario containing multiple heterogeneous task requests. This challenge is further exacerbated when the number of terminal devices increases and multiple edge servers are required to collaborate to handle task requests. To this end, this paper proposes a fine-grained Collaborative DNN Model Selection scheme for heterogeneous edge computing systems (CoMS), aiming to promote cooperation between edge servers and achieve more effective model selection. Specifically, we first design a reinforcement learning scheme with real-time dynamic normalization strategy, aiming to accelerate model convergence and improve the efficiency of model selection. Next, we introduce a model selection strategy based on greedy algorithm and an efficient fine-grained collaborative model selection strategy respectively to promote cooperation between edge servers, thereby further achieving a balance between inference accuracy and overhead. Extensive experimental results show that compared with baselines, our CoMS reduces the average trade-off overhead by 4.2% to 12.8% and improves the success ratio by 3% to 16%.
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