.png){kind=icon}
.png){kind=icon}
Return
MGCO: Mobility-Aware Generative Computation Offloading in Edge-Cloud Systems
.png){kind=icon}
PRE
AI
DOI：10.1109/TSC.2025.3632862.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Mobility introduces significant challenges for optimal computation offloading, latency minimization, and efficient resource utilization in multi-access edge computing (MEC) systems. A key difficulty lies in leveraging real user trajectories to jointly optimize horizontal (inter-edge) and vertical (edge-to-cloud) task offloading decisions. This paper proposes a two-dimensional offloading scheme for a multi-layer edge–cloud architecture that enables collaborative task execution among resource-constrained edge nodes under mobility conditions. We present <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MGCO</b> (Mobility-Aware Generative Computation Offloading), a generative AI–driven Transformer-based sequence-to-sequence Deep Q-Network (s2s-DQN) framework that learns from real-time trajectory data to anticipate user movement and optimize task placement dynamically. The Transformer architecture is adopted because its multi-head self-attention effectively captures long-range dependencies in mobility and task-demand patterns while avoiding vanishing gradients and sequential bottlenecks inherent to LSTM/GRU models. This design enables parallel contextual reasoning and stable autoregressive action generation, supporting real-time offloading decisions within strict operational latency constraints. Experimental results demonstrate that MGCO consistently outperforms existing methods, achieving up to <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">41.61%</b> reduction in turnaround time compared to <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GASTO</b>, and substantial improvements over <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DMQTO</b> and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HMAOA</b>, reaching up to <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">645.40%</b> and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">751.90%</b>, respectively, for longer prediction horizons (48 time slots of 5 seconds each). These results highlight MGCO’s robustness, scalability, and effectiveness in managing complex mobility scenarios in dynamic edge–cloud environments.
Keywords:
Internet of Things
mobility aware edge computing
offloading
sequence to sequence deep Q-learning
transformer
Journal
IF:
5.8
Papers:
2.1K
Citations:
6.5K
Organization
View more organizations