Leveraging Adaptive Intelligence in Edge Computing for Efficient Data Processing and Task Management

The swift progress of Internet of Things (IoT) devices and latency-sensitive submissions has driven the need for efficient data processing and task management in edge computing environments. Traditional edge computing frameworks often face challenges in optimizing resource utilization and handling dynamic workloads. The integration of adaptive intelligence in edge computing has arisen as a vital solution for augmenting data processing and task management efficiency in modern computing environments. This research introduces a novel algorithm, Edge Reinforcement Learning with Context-Aware Tasking (EDREL-CAT), designed to optimize task allocation and resource management at the edge. By utilizing reinforcement learning techniques coupled with context-aware algorithms, the proposed method aims to significantly improve task scheduling and processing efficiency while addressing the constraints inherent in edge computing. An all-inclusive simulation analysis is steered to evaluate the performance of EDREL-CAT against existing algorithms, including traditional scheduling methods and state-of-the-art approaches. The analysis employs various metrics, including task completion time, system throughput, task queue length and fairness index to provide a thorough comparison. The results demonstrate that EDREL-CAT outperforms existing solutions, confirming its potential as an effective tool for enhancing edge computing capabilities.