Resource Integration Method With Cluster Scale Constraints in Mobile-Edge Computing

With the rapid development of 5G and big data technologies, the Internet of Things is increasingly evolving toward applications characterized by massive connectivity and high data throughput. Mobile edge computing (MEC) leverages resource integration to form clusters with collaborative capabilities, which is crucial for addressing the issues of functional disparities and resource limitations in network nodes. This article considers the MEC scenario of network clustering to provide collaborative services through clusters. To achieve effective and balanced network clustering, we formulate a clustering problem that maximizes modularity under a scale constraint. By analyzing the service capacity and content of nodes and the connections between them, we design an attraction function to compute modularity and set upper and lower bounds for cluster scales to ensure controllable cluster scales. We propose a cluster scale-constrained modularity-based Louvain (CSM-Louvain) algorithm, which incorporates splitting and merging mechanisms into the traditional Louvain algorithm to meet the scale constraints. Simulation results demonstrate that the proposed method effectively balances network resources and load distribution, thereby enhancing the quality of MEC collaborative services.