scholarly journals An efficient cloud scheduler design supporting preemptible instances

2019 ◽  
Vol 95 ◽  
pp. 68-78 ◽  
Author(s):  
Álvaro López García ◽  
Enol Fernández del Castillo ◽  
Isabel Campos Plasencia
Keyword(s):  
Scheduler Design

scholarly journals Knowledge-Assisted Deep Reinforcement Learning in 5G Scheduler Design: From Theoretical Framework to Implementation

2021 ◽  
pp. 1-1
Author(s):  
Zhouyou Gu ◽  
Changyang She ◽  
Wibowo Hardjawana ◽  
Simon Lumb ◽  
David McKechnie ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Theoretical Framework ◽  
Scheduler Design

scholarly journals Control-Scheduling Codesign for NCS based Fuzzy Systems

2018 ◽  
Vol 13 (2) ◽  
pp. 251-267 ◽  
Author(s):  
Paul Erick Mendez-Monroy ◽  
Israel Sanchez Dominguez ◽  
Ali Bassam ◽  
Oscar May Tzuc
Keyword(s):  
Embedded System ◽  
Fuzzy Controller ◽  
Hybrid Control ◽  
Computational Cost ◽  
Sampling Period ◽  
Networked Control System ◽  
Fuzzy Scheduling ◽  
The Stability ◽  
Scheduler Design ◽  
Low Computational Cost

In the present paper, a fuzzy codesign approach is proposed to deal with the controller and scheduler design for a networked control system which is physically distributed with a shared communication network. The proposed fuzzy controller is applied to generate the control with different sampling-actuation periods, the configuration supposes a strict actuation period disappears the jitter. The proposed fuzzy scheduling is designed to select the sampling-actuation period. So, the fuzzy codesign reduces the rate of transmission when the system is stable through the scheduler while the controller adjusts the control signal. The fuzzy codesign guarantees the stability of all the system if the network uncertainties do not exceed an upper bound and is a low computational cost method implemented with an embedded system. An unstable, nonlinear system is used to evaluate the proposed approach and compared to a hybrid control, the results show greater robustness to multiple lost packets and time delays much larger than the sampling period. (This paper is an extension of [20]. Reprinted (partial) and extended, with permission based on License Number 4275590998661 IEEE, from "Electrical Engineering, Computing Science and Automatic Control, 2017 14th International Conference on")

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