NPRA: A Novel Predictive Resource Allocation Mechanism for Next Generation Network Slicing

Network slicing is the critical enabler for next-generation mobile networks, which divides the infrastructure into multiple logical networks known as slices. Each logical network supports services with specific throughput and latency requirements. The fifth-generation(5G) and 5G-beyond networks employ more than two slices; hence, it has become necessary to deploy algorithms for efficient resource allocation. However, given the latency-sensitive applications, allocating resources to different slices based on the current demand would be a poor choice. Therefore, resource allocation needs to be performed in advance, which calls for forecasting algorithms predicting future demands. In this paper, we propose a novel predictive network slicing mechanism named NPRA to predict resources dynamically. NPRA predicts the future resource requirements using Unit Time Long Short-Term Memory (UT-LSTM). The predicted demand can be used as an input to any optimization algorithm for the timely allocation of resources. We also develop a 5G simulation testbed to generate datasets for performance study. The results presented demonstrate the effectiveness of NPRA.