Abstract
Mobility introduces significant challenges for optimal computation offloading, latency minimization, and efficient re source 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 MGCO (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 41.61% reduction in turnaround time compared to GASTO, and substantial improvements over DMQTO and HMAOA, reaching up to 645.40% and 751.90%, 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.
Recommended Citation
A. Ghosh et al., "MGCO: Mobility-Aware Generative Computation Offloading in Edge-Cloud Systems.," IEEE Transactions on Services Computing, Institute of Electrical and Electronics Engineers; Computer Society, Jan 2025.
The definitive version is available at https://doi.org/10.1109/TSC.2025.3632862
Department(s)
Computer Science
Publication Status
Early Access
Keywords and Phrases
Internet of Things; Mobility Aware Edge Com-puting; Offloading; Sequence to Sequence Deep Q-learning; Transformer
International Standard Serial Number (ISSN)
1939-1374
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Institute of Electrical and Electronics Engineers; Computer Society, All rights reserved.
Publication Date
01 Jan 2025
