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<p>The spread of mobile devices and IoT sensors demands robust edge computing frameworks capable of maintaining seamless service quality for users on the move. A central challenge is intelligent service migration, which must proactively relocate user services to optimal edge servers by anticipating mobility and adapting to user-specific needs. This paper introduces a comprehensive Context-Aware Service Migration (C-Migrate) framework that synergistically integrates high-fidelity trajectory prediction with context-aware optimization to address this challenge. We propose C-Migrate framework containing two main components: First, we propose&#xa0;SpatioFormer, an encoder-only transformer model that forecasts user mobility with high accuracy. These predictions then inform our Hybrid Simulated Annealing with Deep Q-network refinement (SA-DQN)&#xa0;algorithm, which formulates server selection as a constraint-based facility location problem. Our approach uniquely incorporates a multi-dimensional context model, enabling migrations that are not only spatially efficient but also personalized to dynamic user conditions, such as in mobile healthcare scenarios. Evaluated in a large-scale urban mobility case study with extensive experiments, our framework demonstrates a significant reduction of&#xa0;9–11%&#xa0;in the number of service migrations with the use of trajectory prediction and a significant&#xa0;68–73%&#xa0;reduction in service migrations considering the users’ context, compared to state-of-the-art baselines. This work establishes that the integrated trajectory prediction and context-aware optimization is essential for intelligent service migration, paving the way for more responsive and efficient edge computing ecosystems.</p>

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Context-aware service migration using hybrid SA-DQN in edge computing

  • 期刊论文

  • 发表日期：

    2025年11月7日

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  • Majid Ayoubi,
  • Mingchu Li,
  • Mohammed Albishari

摘要

The spread of mobile devices and IoT sensors demands robust edge computing frameworks capable of maintaining seamless service quality for users on the move. A central challenge is intelligent service migration, which must proactively relocate user services to optimal edge servers by anticipating mobility and adapting to user-specific needs. This paper introduces a comprehensive Context-Aware Service Migration (C-Migrate) framework that synergistically integrates high-fidelity trajectory prediction with context-aware optimization to address this challenge. We propose C-Migrate framework containing two main components: First, we propose SpatioFormer, an encoder-only transformer model that forecasts user mobility with high accuracy. These predictions then inform our Hybrid Simulated Annealing with Deep Q-network refinement (SA-DQN) algorithm, which formulates server selection as a constraint-based facility location problem. Our approach uniquely incorporates a multi-dimensional context model, enabling migrations that are not only spatially efficient but also personalized to dynamic user conditions, such as in mobile healthcare scenarios. Evaluated in a large-scale urban mobility case study with extensive experiments, our framework demonstrates a significant reduction of 9–11% in the number of service migrations with the use of trajectory prediction and a significant 68–73% reduction in service migrations considering the users’ context, compared to state-of-the-art baselines. This work establishes that the integrated trajectory prediction and context-aware optimization is essential for intelligent service migration, paving the way for more responsive and efficient edge computing ecosystems.