An adjustable time slice process scheduling algorithm based on feature similarity clustering

Xiao Gong; Liang Ding

doi:10.1117/12.2684709

19 October 2023 An adjustable time slice process scheduling algorithm based on feature similarity clustering

Xiao Gong, Liang Ding

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127093O (2023) https://doi.org/10.1117/12.2684709
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

In order to improve the efficiency of the operating system scheduler, this paper proposes an adjustable time slice process scheduling algorithm ATSPS (Adjustable Time Slice Process Scheduling) based on feature similarity clustering, which can reduce the cost of the operating system process scheduling time, reduce the process turnaround time, and improve the process execution efficiency. ATSPS forms different process clusters by clustering the characteristics of process burst time, time slice, weight, and context switching times. It uses the same specification time slice for each type of process cluster. Through queue scheduling adjustment, it accelerates the execution efficiency of computationally intensive processes, reduces the number of contexts switching times caused by the expiration of time slice, and saves scheduling time. The scheduling process of robot grabbing program in the operating system is simulated by MATLAB, and the ready queue, blocking queue and execution queue of the operating system are simulated. The robot grabbing program is simulated, including visual process, force control process and posture process. ATSPS is compared with five classical scheduling algorithms. The experimental results show that on the scheduling criteria of three CPU scheduling algorithms, namely, waiting time, turnaround time and context switching times, ATSPS algorithm reduces the time cost of process scheduling, realizes the real-time scheduling of operating system process, and provides guarantee for the realtime control of robot.

Citation Download Citation

Xiao Gong and Liang Ding "An adjustable time slice process scheduling algorithm based on feature similarity clustering", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127093O (19 October 2023); https://doi.org/10.1117/12.2684709

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KEYWORDS

Quantum processes

Fourier transforms

Operating systems

Switching

Quantum switching

Switches

Detection and tracking algorithms

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