Distributed Deep Learning-based Offloading for Mobile Edge Computing Networks
Python code to reproduce our works on Deep Learning-based Offloading for Mobile-Edge Computing Networks [1], where multiple parallel Deep Neural Networks (DNNs) are used to efficiently generate near-optimal binary offloading decisions. This project includes:
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memory.py: the DNN structure for the WPMEC, inclduing training structure and test structure
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data: all data are stored in this subdirectory, includes:
- MUMT_data_3X3.mat: training and testing data sets, where 3X3 means that the user number is 3, and each has 3 tasks.
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main.py: run this file, inclduing setting system parameters
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MEC_env.py: calculation of the underlying energy consumption, input the size of all tasks and offloading decision, and output the calculation results
- Liang Huang, Xu Feng, Anqi Feng, Yupin Huang, and Li Ping Qian, "Distributed Deep Learning-based Offloading for Mobile Edge Computing Networks," in Mobile Networks and Applications, 2018, DOI:10.1007/s11036-018-1177-x.
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Tensorflow
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numpy
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scipy
run the file, main.py