Early Exiting-Aware Joint Resource Allocation and DNN Splitting for Multisensor Digital Twin in Edge–Cloud Collaborative System

In this article, we address an edge computing resource allocation and deep neural network (DNN) splitting problem in an edge–cloud collaborative system to minimize the task execution time of a multisensor digital twin (DT), where the constituent tasks of the multisensor DT are employed by DNN models with both split computing and early exit structures. To this end, we develop an early exiting-aware joint edge computing resource allocation and DNN splitting (ERDS) framework that optimally solves the problem. In the framework, the problem is reformulated into a nested optimization problem consisting of an outer edge computing resource allocation problem and an inner DNN splitting problem which considers early exiting. Based on the nested structure, the framework can efficiently solve the problem without having to consider the ERDS jointly. As components of the framework, we develop an edge computing resource allocation algorithm that exploits the mathematical structure of the outer problem; we also develop an optimal DNN splitting algorithm and a heuristic algorithm that identifies suboptimal solutions but has lower computational complexity. Through the simulation, we demonstrate that our proposed framework effectively outperforms the other state-of-the-art baselines in terms of the task execution time of the multisensor DT in different environments, which shows that our proposed framework is applicable in practical multisensor DTs.