Machine Learning-based Slice allocation Algorithms in 5G Networks

• Published in: 2019 International Conference on Advances in Computing, Communication and Control (ICAC3) pp. 1 - 4
• Main Authors: Gupta, Rohit Kumar, Misra, Rajiv
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2019
• Subjects: 5G mobile communication; Availability; Latency; Machine learning algorithms; Network slicing; NFV; Quality of experience; Quality of service; Reliability; Resource management; SDN; SLA; Software defined networking; Throughput; Virtualization
• DOI: 10.1109/ICAC347590.2019.9036741

One of the primary aims for 5G networks is the flexibility to support vertical services as the primary requirement. The use cases of 5G networks are AR/VR, eHealth, Video Streaming, automation works, automated vehicles, etc. All use cases are associated with its requirements like availability, reliability, latency, and throughput in terms of Quality of Services (QoS) and Quality of Experience (QoE). In 5G, the Network-Functions Virtualization (NFV) and Software-Defined Networking (SDN) combined to provide the concept of Network Slicing. Network Slicing runs multiple logical networks as virtually independent operations over shared physical infrastructure. In this work, we address the slice allocation problem using machine learning algorithms with the dataset. The performance evaluation of the slice allocation algorithm has addressed using different machine learning models. In the simulation, we demonstrated that our proposed method allocates the best slice for service with high accuracy.