User coverage-oriented k-model deployment in edge computing systems

Abstract

This paper focuses on the key problem of maximizing user service coverage under a limited deployment budget in edge computing scenarios. To meet the demand of service providers to select $k$ locations from a set of candidate edge servers for model deployment so as to maximize coverage of end-user groups, we formally define the $k$-Model Deployment Coverage Maximization ($k$MD-C) problem and prove that it is NP-hard. For this problem, we propose two methods: MD-O, an exact benchmark method based on integer programming suitable for small-scale problems, and MD-A, an efficient heuristic algorithm with superior empirical performance. Experimental validation on real-world datasets shows that MD-A significantly outperforms several state-of-the-art baseline methods in terms of coverage performance and computational efficiency, demonstrating its superior practicality and scalability for solving large-scale $k$MD-C problems. This study provides a theoretical foundation and efficient tools for the scientific deployment of edge intelligent services, and helps improve service coverage and user satisfaction under resource-constrained conditions.