Optimal Task Allocation for Time-Varying Edge Computing Systems with Split DNNs

Abstract

Many modern applications rely on complex machine learning algorithms, such as Deep Neural Networks (DNNs), to analyze images. However, both mobile and edge computing strategies may fail to provide satisfactory performance in some parameter regions. To mitigate this issue, the research community recently proposed methods to split the execution of DNNs to optimize the balance between computing load allocation and channel usage. Building on this set of results, this paper presents an optimization framework that enables the dynamic control of how images are processed in mobile device-edge server systems. The system is modeled as a Markov process, and a Linear Fractional Program is defined to identify the optimal stationary state-action distribution minimizing the overall average inference time under a constraint on the number of discarded images. Results indicate the advantage of using a dynamic control strategy with respect to available fixed strategies.