Main Reference PaperLearning for Smart Edge: Cognitive Learning-Based Computation Offloading, Mobile Networks and Applications, 2018 [Java/EdgeCloudSim].
  • In this paper, first introduces a new task offloading architecture which includes computation task cognitive layer, edge resource cognitive layer, and global management cognitive layer. Then, it gives the optimization problem of computation offloading under multi-user and multi-server scene. Also, it proposes the CLCO scheme and study about the computing problem of the learning-based computation offloading algorithm.

Description
  • In this paper, first introduces a new task offloading architecture which includes computation task cognitive layer, edge resource cognitive layer, and global management cognitive layer. Then, it gives the optimization problem of computation offloading under multi-user and multi-server scene. Also, it proposes the CLCO scheme and study about the computing problem of the learning-based computation offloading algorithm.

  • To reduce task duration and energy consumption.

Aim & Objectives
  • To reduce task duration and energy consumption.

  • The task execution time and energy consumption are considered locally and on edge devices for further optimization of objectives.

Contribution
  • The task execution time and energy consumption are considered locally and on edge devices for further optimization of objectives.

  • 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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