Dynamic Scheduling for Stochastic Edge-Cloud Computing Environments using A3C learning and Residual Recurrent Neural Networks

The ubiquitous adoption of Internet-of-Things (IoT) based applications has resulted in the emergence of the Fog computing paradigm, which allows seamlessly harnessing both mobile-edge and cloud resources. Efficient scheduling of application tasks in such environments is challenging due to constrained resource capabilities, mobility factors in IoT, resource heterogeneity, network hierarchy, and stochastic behaviors... (read more)

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Methods used in the Paper


METHOD TYPE
Dense Connections
Feedforward Networks
Softmax
Output Functions
Entropy Regularization
Regularization
Convolution
Convolutions
A3C
Policy Gradient Methods