Evaluation method for cross-domain data consistency in distributed storage under cloud platforms

The issue of ensuring data consistency across different domains in cloud-based distributed storage systems is a major problem, being mainly due to the maintenance of synchronization and the reliability of the distribution of data across various domains. In this research, these issues are tackled by the introduction of the Addax Optimization Algorithm (AOA), which is a new method that synergistically combines optimization techniques with K-means clustering to support data consistency. The overall goal is to attain simultaneous reduction of consistency latency and maximization of packet delivery ratio (PDR) and data integrity in multi-domain settings. In the course of the experimental evaluation, the new technique showed better performance than the conventional approaches such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) on the important parameters of latency, PDR, and data integrity, thus proving its suitability for real-world cloud-based distributed systems. The findings imply that AOA is a good choice for facilitating cross-domain data consistency with remarkable advantages in terms of scalability and reliability even in diverse network conditions.