SITOff: Enabling Size-Insensitive Task Offloading in D2D-Assisted Mobile Edge Computing
Hu, Zheyuan Niu, Jianwei Ren, Tao Liu, Xuefeng Guizani, Mohsen
Hu, Zheyuan
Niu, Jianwei
Ren, Tao
Liu, Xuefeng
Guizani, Mohsen
Supervisor
Department
Machine Learning
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Type
Journal article
Date
2025
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Language
English
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Abstract
Mobile edge computing (MEC), along with device-to-device (D2D) assisted MEC (D-MEC), are promising technologies that could improve the quality-of-experience for mobile devices (MDs) by offloading their tasks to edge servers or nearby idle MDs. There is a popular trend to develop distributed task offloading algorithms using multi-agent reinforcement learning (MARL), whose adoption of central critics during training makes the offloading still size-sensitive. Therefore, this paper proposes a Size-Insensitive Task Offloading (SITOff) algorithm for D-MEC based on fully-distributed offloading without maintaining any central venue. Specifically, taking advantage of the inherent graph-like structure of D-MEC, SITOff adopts graphs to represent MDs' states and relationships and form each MD's local knowledge about D-MEC through graph computation. Furthermore, considering the limitation of local knowledge in performing whole performance-oriented offloading, each MD utilizes D2D-transmitting to exchange knowledge with its neighbors and form a comprehensive knowledge about D-MEC to enhance the coordination of distributed offloading. Additionally, regarding the different impacts of neighbors' knowledge, each MD leverages attention mechanisms to selectively learn its neighbors' knowledge during knowledge-exchange. Extensive experimental results show the superiority of SITOff over state-of-the-art MARL-based offloading algorithms in D-MEC with various MDs, and the easy collaboration of SITOff with curriculum-learning for large-scale D-MEC offloading.
Citation
Z. Hu, J. Niu, T. Ren, X. Liu, and M. Guizani, “SITOff: Enabling Size-Insensitive Task Offloading in D2D-Assisted Mobile Edge Computing,” IEEE Trans Mob Comput, 2024, doi: 10.1109/TMC.2024.3483951.
Source
IEEE Transactions on Mobile Computing
Conference
Keywords
Device to device, mobile edge computing, reinforcement learning, task offloading
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Publisher
IEEE