Joint Optimization of Model Deployment for Freshness-Sensitive Task Assignment in Edge Intelligence

Edge Intelligence aims to push deep learning (DL) services to network edge to reduce response time and protect privacy. In implementations, proximity deployment of DL models and timely updates can improve the quality of experience (QoE) for users, but increase the operation cost as well as pose a challenge for task assignment. To address the challenge, a joint online optimization problem for DL model deployment (including placement and update) and freshness-sensitive task assignment is formulated to improve QoE and application service provider (ASP) profit. In the problem, we introduce the age of information (AOI) to quantify the freshness of the DL model and represent user QoE as an AOI based utility function. To solve the problem, an online model placement, update, and task assignment (MPUTA) algorithm is proposed. It first converts the time-slot coupled problem into a single time-slot problem using the regularization technique, and decomposes the single time-slot problem into model deployment and task assignment subproblems. Then, using the randomized round technique to deal with the model deployment subproblem and the graph matching technique to solve the task assignment subproblem. In simulation experiments, MPUTA is shown to outperform other benchmark algorithms in terms of both user QoE and ASP profit.