Ensemble Learning-based Network Data Analytics for Network Slice Orchestration and Management: An Intent-Based Networking Mechanism
Khizar Abbas, Talha Ahmed Khan, Muhammad Afaq, Wang‐Cheol Song
Abstract
5G technology come up with many innovative features compared to legacy networks, such as network slicing that envisioned a wide variety of services from different customers, network operators, and industrial verticals. Network slicing ensures dedicated and isolated resources to each of the services. The autonomous orchestration and management of end-to-end (e2e) network slicing is critical due to the complex network configuration for the underlying infrastructure. On the other side, data analytics seems promising to manage and control the underlying network resources proactively. So, network data analytics function (NWDAF) has been introduced in 5G service-based architecture (SBA), which enables network operators to use various artificial intelligence (AI) and machine learning (ML) techniques. Therefore, this paper presents a closed-loop mechanism that has two parts: 1) an intent-based networking (IBN) mechanism for efficient control, orchestration, and management of e2e network slicing. 2) A data analytics mechanism that uses novel hybrid ensemble learning (EL) algorithms for network resource utilization prediction and anomaly detection and mitigation. The results show that the proposed stacking ensemble learning (STEL) model for resource utilization prediction enhanced accuracy by approximately up to 20% and reduced the error by 45% compared to the state-of-the-art models. In addition, ML models assist the IBN platform in updating and managing the network resources proactively.