Python code to reproduce our soCoM model, which utilizes users’ behavio 597F r prediction methods to optimize task offloading in edge computing environments. This is the code of paper in title 'Semi-online Computational Offloading by Dueling Deep-Q Network for User Behavior Prediction'. DOI: 10.1109/ACCESS.2020.3004861
It includes:
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soCoM.py: The system model for soCoM, including definition of the task, user, MEC server, communication model, computing model, and energy consumption model.
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OFFLOAD.py: RL offloading training process.
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RLbrain*.py: RL algorithm of DQN, Dueling DQN, Double DQN, Prioritized replay.
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Simulation.py: run this file for soCoM, creating a simulated environment.
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soCoMM.py, OFFLOADM.py, Simulation-multi.py: Multiple servers senario.
- SimPy: https://simpy.readthedocs.io/en/latest/
- Tensorflow 1.0
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For the soCoM simulation, run the file Simulation.py.
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For changing the numbers of user equipment, change the global variable 'UN' in the file soCoM.py.
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For changing the DQN algorithms, change the import of package in the file OFFLOAD.py.
- Shinan Song, songshinan AT 163.com