Energy-efficient offloading framework for mobile edge/cloud computing based on Convex Optimization and Deep Q-Network

Energy efficiency is one of the most critical aspects of modern computing paradigms due to minimizing carbon footprint and lowering operational costs. To achieve efficiency, the typical approach is to address the source of energy consumption and apply the appropriate strategies for energy savings. In this paper, based on an offloading framework for edge and cloud computing, we proposed a comprehensive methodology that leverages predictive analysis and convex optimization techniques to achieve efficiency in power utilization. This methodology aimed to reduce the power consumption of edge/cloud computing clusters while maintaining an acceptable quality of service. The core idea was to enhance the historical data in the first place by using the prediction. This predictive historical data revealed the trend of computational resource allocation. Subsequently, the convex optimization technique coupled with the Deep Q-Network (DQN) model was employed to formulate and schedule the distribution of the offloaded tasks. By engaging this combination, the offloading framework could produce a near-optimal and adaptive energy decision, which helps achieve energy efficiency. The experimental results showed that the proposed methodology could obtain significant energy savings while maintaining a suitable level of performance compared to other state-of-the-art approaches.