EDGE COMPUTING-DRIVEN RESOURCE ALLOCATION FOR LATENCY-SENSITIVE 5G APPLICATIONS

In 5G networks, the exponential growth of latency-sensitive applications has improved requirement of effective resource allocation. Traditional models often face struggle to satisfy the high latency constraints, due to the inherent delays imposed by the centralised processing. To overcome these issues, this paper proposed a hybrid method for resource allocation in edge computing at the network’s edge. To optimise the resource allocation while ensuring the low latency and higher energy efficiency, the proposed method incorporates artificial intelligence (AI) with optimisation model. The proposed method consists of a multi-layered architecture that begins with data collection at edge devices, followed by pre-processing and utilises the Long Short-Term Memory (LSTM) model for feature extraction. Real-time resource demand forecasting and task distribution analysis are utilised by the AI Deep reinforcement learning (DRL) and the Special Forces Algorithm (SFA) at edge nodes. This method is employed to adaptively distribute the resources based on network conditions and application requirements. Comprehensive simulation results display that the proposed method increased energy efficiency while lowering end-to-end latency that surpasses the conventional approaches. Additionally, the system significantly enhances its performance by meeting the user demands without losing efficiency. This study elaborates the edge computing potential to overcome the drawbacks of existing cloud-based architectures and provides a reliable solution for 5G applications sensitive to latency.