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In this project ,a component migration strategy in an NFV-based hybrid cloud/fog system considering the mobility of both end-users and fog nodes based on Deep Reinforcement Learning is implemented.

Python 100.00%

reinforcement-learning-based-optimizationframework-for-application-componentmigration-in-nfv-cloud-f's Introduction

Reinforcement-Learning-based-OptimizationFramework-for-Application-ComponentMigration-in-NFV-Cloud-Fog Environments

In this project, a component migration strategy in an NFV-based hybrid cloud/fog system considering the mobility of both end-users and fog nodes based on Dee Reinforcement Learning is implemented. The code is inspired from "BinPacking_Neural_Combinatorial_Optimization" Model built by Ruben Solozabal. Readers are encourged to first get familiar with DRL based binpacking simulation code as DRLCM is a dynamic and more complex model. The following the Link to the bin_packing_combinatorial_Optimization code is provided.

https://github.com/rubensolozabal/BinPacking_Neural_Combinatorial_Optimization

Requirement

Tensorflow 1.14.0

Python 3.6

Author

Negar Afrasiabi, PhD student at the Concordia University

Contact me: [email protected]

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