Fair DNN Model Selection in Edge AI via A Cooperative Game Approach

Edge intelligence is an emerging paradigm that leverages edge computing to pave the last-mile delivery of artificial intelligence (AI). To adapt to the resource restriction, model selection which adaptively selects DNN model variants is widely applied to shape the resource demand of edge AI inference tasks. Unfortunately, in current edge AI serving systems, applications are suffering unfairness since the DNN model selection is performed in a best-effort manner to maximize the system-wide inference accuracy. To achieve a predictable inference accuracy for the applications, edge AI serving systems should guarantee the minimum inference accuracy in a fair fashion at the application level. At the same time, edge resources should be efficiently utilized to minimize operational costs. In this paper, we model the edge DNN model selection problem as a Nash Bargaining Game (NBG), and propose the model selection principles by guaranteeing a base accuracy for each application. Based on the rigorous cooperative game-theoretic approach, we design an approximate algorithm to achieve computationally-efficient and fair model selection, corresponding to the Nash Bargaining Solution (NBS). With extensive trace-driven simulations, we show that our strategy can meet two desirable requirements towards the predictable inference accuracy for applications as well as low operational costs for the system.