#Histogram maker openmp generator
A generator compartment of the latest control attributes and the Nearest neighbor algorithm achieves better performance with a low-mean flow time and mean-tardiness.īased on the enhanced ant colony algorithm, a task-scheduling and optimization algorithm model for the distributed cyber-physical system is evaluated. Three machine-learning algorithms are proposed to solve the dispatch rules used for dynamic schedule jobs in Flexible Manufacturing Systems (FMS). For small core produces a higher performance of 9.7% compared to fairness –aware and dynamic- ANN outshine by 1.5%. When compared with conventional schedulers, the experimented scheduler achieves 6.5% of fairness – awareness for big core. ANN predictor detects the core behavior on distinct core types for a small dataset. Deep Q- network with deep reinforcement scheduling algorithm outperforms better after 40–50 epoch of training.Īnother network-based heterogeneous scheduler aims to enhance the model throughput by employing the Artificial Neural Network (ANN). Compared to the round-robin algorithm, the Markov decision process (MDP) is addressed to solve sensor-scheduling and access of the network on a minimal waiting time. Therefore, it is needed to decide the priority among the sensors to have network access. The number of sensors will be more than the number of wireless channels. In the cyber-physical system, the sensors should be effectively scheduled for transmission through a central gateway. There are many scheduling algorithms and solutions given in the literature. A load-aware multiprocessor-scheduler will be the viable solution for the above issue.
But, ensuring that the deadline meets all the time-critical application tasks, is quite a challenging research issue. The processors execute different task sets in a distributed fashion. Overloading task execution in the cyber-physical system's multiprocessor environment is one of the main issues, especially for time-critical cyber-physical systems.
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