HDGC-hybrid task offloading framework using deep reinforcement learning and genetic algorithms for 6G edge cloud

Abstract

The rapid evolution of 6G networks has brought new challenges in the domain of task offloading (TO), particularly within edge computing environments that are heavily reliant on the internet of things (IoT). Traditional TO methods that based on rule-based heuristics or shallow learning techniques fail to adapt efficiently to the dynamic, unpredictable network conditions, resource heterogeneity, and varying task demands. The proliferation of edge computing, the IoT, and 6G networks has introduced new challenges in TO due to dynamic network conditions, resource heterogeneity, and unpredictable task demands. To address these challenges, this work proposes an innovative TO method that integrates deep reinforcement learning (DRL) with heuristic search methods. The combination of DRL and heuristic algorithms enhances adaptability, convergence speed, and decision-making efficiency, making it well-suited for real-time TO in complex and unpredictable environments This paper proposes a novel hybrid TO framework that integrates DRL with genetic algorithms (GA) to address these challenges. The proposed hybrid optimization technique offer promising solutions by leveraging the strengths of individual approaches to balance competing objectives, such as energy consumption, task completion time, and resource utilization. This method explores optimization strategies to enhance TO efficiency in decentralized environments mainly focusing on optimizing energy use while ensuring performance metrics like latency, throughput, and task deadlines are met.