Double Deep Q-Network-Based Time and Energy-Efficient Mobility-Aware Workflow Migration Approach

With the emergence of the Fog paradigm, the relocation of computational capabilities to the network’s edge has become imperative to support the ever-growing requirements of latency-sensitive, data-intensive, and real-time decision-making applications. In dynamic and mobile environments, services must adapt to accommodate the mobility of users, resulting in frequent relocations across computing nodes to ensure seamless user experiences. However, these migrations incur additional costs and potentially degrade the Quality of Service (QoS) parameters. In this paper, we propose a mobility-aware workflow migration approach based on Deep Reinforcement Learning (DRL). This approach aims to minimize the system’s overall delay and energy consumption by optimizing the number of workflow task migrations, considering resource performance and network conditions in different regions. The problem is first formulated as a Markov Decision Process (MDP), and then a Double Deep Q-network (DDQN) algorithm is proposed to identify the optimal policy for workflow offloading and migration. Comprehensive experiments have been conducted and the results demonstrate that our approach outperforms significantly the existing approaches.