A Novel, Efficient, Green and Real-Time Load Balancing Algorithm for 5G Network Measurement Report Collecting Clusters

With the wide application of data mining and deep learning in mobile cellular network operation and maintenance, network measurement report (MR) plays an increasingly important role in artificial intelligence for IT operations (AIOps). For the integrity of MR reported by the operation and maintenance (OM) proxy of base station, existing collecting methods are typically based on static distributed clustering. Due to the lack of effective load balancing scheme, nevertheless, these methods typically result in some issues, e.g., low collecting efficiency, poor scalability, and excessive number of servers. Thus, in this work, leveraging the historical law of uploading MR for load forecasting, we propose the weighted least-connection load balancing algorithm (LPWLC) based on load forecasting. First, the historical law of reported MR is utilized to predict the load. Second, using the strategy of static binding and dynamic load adjustment, we bind OM with the assigned server in one cycle, calculate the server load in real-time, and evaluate the server weight by the load of each server. Finally, real-time load adjustment is carried out in line with the number of request connections and the weight of servers. Compared with the existing ones, the proposed algorithm could remove backup servers, thereby effectively reducing the cost and power consumption. Compared with the existing methods, this method has improved the load balancing degree by 28%, and reduced the energy consumption by 104[Formula: see text]W per hour.