Traffic Profiling and Secure Virtualized Data Handling of 5G Networks via MinIO Storage

Abstract

In the modern era of 5th generation (5G) networks, the data generated by User Equipments (UE) has increased significantly, with data file sizes varying from modest sensor logs to enormous multimedia files. In modern telecommunications networks, the need for high-end security and efficient management of these large data files is a great challenge for network designers. The proposed model provides the efficient real-time virtual data storage of UE data files (light and heavy) using an object storage system MinIO having inbuilt Software Development Kits (SDKs) that are compatible with Amazon (S3) Application Program Interface (API) making operations like file uploading, and data retrieval extremely efficient as compared to legacy virtual storage system requiring low-level HTTP requests for data management. To provide integrity, authenticity, and confidentiality (integrity checking via an authentication tag) to the data files of UE, the encrypted algorithm 256-bit oriented-Advanced Encryption Standard (256-AES) in Galois/Counter Mode (GCM) is utilized in combination with MinIO. The AES-based MinIO signifies in more secure and faster approach than older models like Cipher Block Chaining (CBC). The performance of the proposed model is analyzed using the Iperf utility to perform the Teletraffic parametric (bandwidth, throughput, latency, and transmission delay) analysis for three different cases namely: (a) light UE traffic (uploading and retrieval) (b) heavy UE traffic (uploading and retrieval) and (c) comparison of Teletraffic parameters namely: bandwidth (B_ava), throughput (T_put), data transfer (D_Trans), latency (L_ms), and transmission delay (T_Delay) obtained from proposed method with legacy virtual storage methods. The results show that the suggested MinIO-based system outperforms conventional systems in terms of latency, encryption efficiency, and performance under varying data load conditions.