Main Reference PaperDistributed Deep Learning-based Offloading for Mobile Edge Computing Networks, Mobile Networks and Applications, 2018 [Java/EdgeCloudSim].
  • This project presents a distributed deep learning-based offloading algorithm, DDLO, for MEC networks. The algorithm takes advantages of multiple deep neural networks (DNNs) and generates close-to-optimal solutions without manually labeled data. Numerical results have validated the accuracy of the proposed algorithm and the performance advantage compared with the existing deep Q-network algorithm. Furthermore, the proposed DDLO algorithm can generate near-optimal offloading decisions in less than one second, whose computation time is independent of the number of DNNs.

Description
  • This project presents a distributed deep learning-based offloading algorithm, DDLO, for MEC networks. The algorithm takes advantages of multiple deep neural networks (DNNs) and generates close-to-optimal solutions without manually labeled data. Numerical results have validated the accuracy of the proposed algorithm and the performance advantage compared with the existing deep Q-network algorithm. Furthermore, the proposed DDLO algorithm can generate near-optimal offloading decisions in less than one second, whose computation time is independent of the number of DNNs.

  • To minimize the overall system utility including both the total energy consumption and the delay in finishing the task.

Aim & Objectives
  • To minimize the overall system utility including both the total energy consumption and the delay in finishing the task.

  • It adopts a shared replay memory to store newly generated offloading decisions which are further to train and improve all DNNs.

Contribution
  • It adopts a shared replay memory to store newly generated offloading decisions which are further to train and improve all DNNs.

  • Java Development Kit 1.8.0, MySQL 5.5.40. Cloudsim-4.0 with WorkflowSim-1.0.

  • Netbeans 8.0.1, J2SE.

Software Tools & Technologies
  • Java Development Kit 1.8.0, MySQL 5.5.40. Cloudsim-4.0 with WorkflowSim-1.0.

  • Netbeans 8.0.1, J2SE.

  • 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.

Professional Ethics: We S-Logix would appreciate the students those who willingly contribute with atleast a line of thinking of their own while preparing the project with us. It is advised that the project given by us be considered only as a model project and be applied with confidence to contribute your own ideas through our expert guidance and enrich your knowledge.

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