An edge computing offloading strategy based on multi-dimensional attributes and distributed deep learning

doi:10.1504/IJSNET.2026.151235

An edge computing offloading strategy based on multi-dimensional attributes and distributed deep learning

2026-01-01

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She, Dong *

DOI：10.1504/IJSNET.2026.151235

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With the explosive growth of smart terminal devices and the wide adoption of latency-sensitive applications, the traditional cloud computing model is difficult to meet the demands of ultra-low latency and high privacy protection. Therefore, this paper proposes a distributed deep reinforcement learning offloading strategy based on multi-dimensional joint modelling of task, device, and environment attributes. A state space integrating multi-dimensional attributes is constructed to achieve comprehensive awareness of the system state; a distributed asynchronous deep Q-network framework is designed to realise knowledge sharing through local model co-training among multiple edge nodes. Experimental results show that this approach can reduce the average task processing latency by 4.8% and the overall system energy consumption by 5.3%. This research provides a practical solution for computational offloading in resource-constrained edge scenarios that balances efficiency and energy consumption.

Keywords:

multi-dimensional attributes

distributed

deep learning

edge computing offloading

An edge computing offloading strategy based on multi-dimensional attributes and distributed deep learning

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