Deep reinforcement learning for energy and time optimized scheduling of precedence-constrained tasks in edge–cloud computing environments

Author:

Jayanetti AmandaORCID,Halgamuge SamanORCID,Buyya Rajkumar

Publisher

Elsevier BV

Subject

Computer Networks and Communications,Hardware and Architecture,Software

Reference33 articles.

1. Theoretical modelling of fog computing: a green computing paradigm to support IoT applications;Sarkar;Iet Netw.,2016

2. Fog computing may help to save energy in cloud computing;Jalali;IEEE J. Sel. Areas Commun.,2016

3. Machine learning based timeliness-guaranteed and energy-efficient task assignment in edge computing systems;Sen,2019

4. Dynamic scheduling for stochastic edge-cloud computing environments using a3c learning and residual recurrent neural networks;Tuli;IEEE Trans. Mob. Comput.,2020

5. Saving time and cost on the scheduling of fog-based IoT applications using deep reinforcement learning approach;Gazori;Future Gener. Comput. Syst.,2019

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