Oct 22, 2019 · In this paper, we propose a Fog Scheduling Decision model based on reinforcement R-learning, which focuses on studying the behavior of service requesters.
Dec 9, 2024 · In this paper, we propose a Fog Scheduling Decision model based on reinforcement R-learning, which focuses on studying the behavior of service ...
In this paper, we propose a Fog Scheduling Decision model based on reinforcement R-learning, which focuses on studying the behavior of service requesters and ...
In this paper, we propose a \textbf{Fog Scheduling Decision} model based on reinforcement R-Learning, which focuses on studying the behavior of service ...
A Reinforcement Learning model deployed on road side units to predict on-demand placement of microservices on cluster or caching at RSU.
Peter Farhat, Hani Sami, Azzam Mourad. Reinforcement R-learning Model for Time Scheduling of On-demand Fog Placement On-demand Fog Scheduling pdf. Authors ...
Reinforcement R-learning model for time scheduling of on-demand fog placement. P Farhat, H Sami, A Mourad. the Journal of Supercomputing 76 (1), 388-410, 2020.
Reinforcement R-learning model for time scheduling of on-demand fog placement. Article. Full-text available. Jan 2020; J SUPERCOMPUT. Peter Farhat · Hani ...
We propose a Deep Reinforcement Learning-based IoT application Scheduling algorithm, called DRLIS to adaptively and efficiently optimize the response time.
In this paper, we use a Reinforcement Learning approach to design such an algorithm starting from the power-of-random choice paradigm, used as a baseline.
Missing: R- placement.