The problem of computing first edge server deployment is a fundamental problem in computing first network. In the actual scenario, the computing first edge server is close to computing power resources and provides access services for them to join the computing first network. However, the structure of computing resources is often determined by the actual demand, and changes with the change of demand. Under the constraint of computing first edge server resources, how to reasonably deploy computing first edge servers to ensure the effective construction of computing networks has become a hot topic of concern for all sectors. Firstly, this paper analyzed the deployment problem of computing first edge servers and transformed it into a multiobjective optimization problem with constraints. It proposed an improved genetic algorithm to address this issue. The advantages of this algorithm were as follows. It found non repetitive feasible solutions as the initial population provided more room for selection operations. When selecting, it adopted an individual balanced selection strategy to ensure the diversity and decentra-lization of the iterative population. When crossing and mutating, it adopted different types of random two point crossing and rotating random single point mutation strategies, thereby ensuring the diversity and diversity of the newborn population. The experiments is verified by resources deviation rate, load error rate, convergence rate. And expectation solution error rate shows that the algorithm is very effective and reasonable.

[2023-11-01] Accepted Paper /doc/2023.08.0391/v1

[2024-05-05] Printed Article /doc/2023.08.0391/v2