Enhancing Migration and Decentralization Strategies in Nodes Clustering

Edge computing architecture enables faster processing by minimizing latency and lag. However, when a mobile user moves away from the service coverage of the associated edge server, the connection gradually disappear, which leads increase in the responding time that can result network performance degradation, dramatic drops in QoS, and even termination of active edge services. By using migration service, the problems of user mobility and variations demand are reduced, the it reduces the latency and improve user experience. A dynamic decentralized data replica placement and management strategy works in edge nodes. It is an infrastructure distributing the computing resources and data across multiple nodes instead of a central location. A decentralized network enhances privacy, improves performance and reliability with low cost. When edge computing and decentralized model are combined, a powerful system is achieved that can drive unprecedented levels of efficiency and scalability in processing massive amounts of data. Clustering system performs the process of using nodes and placing them in independent groups works to achieve a common goal allows distributing the pressure on the network consisting of a large number of parallel individual tasks between nodes, where the cluster system divides the request or tasks between different modes, to avoid overloading load balancing. This article discusses and presents distributing nodes in edge computing networks by using k-means clustering algorithm. It implements the clustered nodes and provides a mechanism to improve an efficient acceleration of data migration on network with lower traffic. © 2025 Elsevier B.V., All rights reserved.