Main ReferenceA Double Deep Q-learning Model for Energy-efficient Edge Scheduling, IEEE Transactions on Services Computing, 2018[Java]

  • A double deep Q-learning model is proposed for energy-efficient edge scheduling (DDQ-EES). The double deep Q-learning model includes a generated network for producing the Q-value for each DVFS algorithm and a target network for producing the target Q-values to train the parameters. Besides, the rectified linear units (ReLU) function is used as the activation function in the double deep Q-learning model, to avoid gradient vanishing. Finally, a learning algorithm based on experience replay is developed to train the parameters of the proposed model.

Description
  • A double deep Q-learning model is proposed for energy-efficient edge scheduling (DDQ-EES). The double deep Q-learning model includes a generated network for producing the Q-value for each DVFS algorithm and a target network for producing the target Q-values to train the parameters. Besides, the rectified linear units (ReLU) function is used as the activation function in the double deep Q-learning model, to avoid gradient vanishing. Finally, a learning algorithm based on experience replay is developed to train the parameters of the proposed model.

  • To reduce the dynamic energy consumption and for energy-efficient edge scheduling.

Aim & Objectives
  • To reduce the dynamic energy consumption and for energy-efficient edge scheduling.

  • Improvement of the training efficiency is considered.

Contribution
  • Improvement of the training efficiency is considered.

  • M.E / M.Tech / MS / Ph.D.- Customized according to the client requirements.

Project Recommended For
  • M.E / M.Tech / MS / Ph.D.- Customized according to the client requirements.

  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

Order To Delivery
  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

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