Aiming at the contradiction between the dynamic security threats and the efficiency of load balancing faced by the shipping cloud platform during the peak logistics period, this paper proposes a collaborative optimization strategy of server baseline security and load balancing based on genetic algorithm (GA). This strategy constructs a double-layer closed-loop architecture: the upper layer adopts GA-ACO (Ant Colony Optimization) hybrid algorithm, which integrates the real-time load and safety score of nodes into the multi-objective optimization model at the same time, so as to realize safety-aware and efficient traffic scheduling; The lower layer uses GA to dynamically adjust the security baseline parameters such as firewall rules and access control list (ACL), and takes the security incident rate and performance index as fitness functions to realize the adaptive change of the baseline with the load. The experiment is carried out in a shipping cloud environment that simulates 10 heterogeneous servers, introducing sudden traffic and real attack scenarios. The results show that, compared with polling, weighted minimum connection and standard ACO, this strategy can reduce the incidence of security incidents by about 60-70% at the expense of only 4-5 ms response time, and the distribution of resource utilization is the most concentrated without abnormal points, which significantly improves the security and operational efficiency of the platform in a dynamic threat environment.
Wang et al. (Sun,) studied this question.