Rapid expansion of the Internet of Things (IoT) and mobile applications has increased the demand for low latency and efficient computational resource management. Although offering high processing power, cloud computing suffers from network congestion and latency issues, making edge computing a viable alternative. Task offloading in cloud-edge environments is crucial for optimizing resource allocation, reducing delays, and enhancing system performance. For intelligent job offloading in an Edge-Cloud architecture, this study suggests a hybrid reinforcement learning model based on Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO). The model is trained on the datasets simulated in EdgeCloudSim simulator and consisting of tasks, network conditions, computational capabilities of servers, and energy efficiency constraints. The results of the experiment shows that the suggested model considerably reduces task execution time, lowers energy usage, and boosts system efficiency. This work provides a robust deep reinforcement learning-based solution for optimizing task offloading in future edge-cloud computing infrastructures.
Baral et al. (Thu,) studied this question.
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