Welcome to Francis Academic Press

Academic Journal of Engineering and Technology Science, 2024, 7(3); doi: 10.25236/AJETS.2024.070307.

Application Prospect of Deadlock Detection and Removal Technology in Modern Operating System

Author(s)

Shukun Wu, Zhongdong Wang, Zihan Yu, Xiangxin Li, Jianhao Lin, Zhensong Chen

Corresponding Author:
Zhongdong Wang
Affiliation(s)

School of Mathematics & Computer Science, Guangxi Science & Technology Normal University, Laibin, China

Abstract

Deadlock refers to the phenomenon that the system cannot work normally due to the interaction of more than two processes. In the modern operating system, the complexity, application difficulty and the range of participants of the system are getting higher and higher, so the detection and removal technology of deadlock is becoming more and more important. This paper reviews the basic technology, application status and shortcomings of deadlock detection and removal, and makes a preliminary prediction for the future development.

Keywords

Deadlock, Detection, Cancellation, Overview, Expectation

Cite This Paper

Shukun Wu, Zhongdong Wang, Zihan Yu, Xiangxin Li, Jianhao Lin, Zhensong Chen. Application Prospect of Deadlock Detection and Removal Technology in Modern Operating System. Academic Journal of Engineering and Technology Science (2024) Vol. 7, Issue 3: 48-53. https://doi.org/10.25236/AJETS.2024.070307.

References

[1] Song Dan, Li Yadong. Improvement and implementation of banker algorithm [J]. Computer Age, 2021 (06): 64-67

[2] Fan Chengya. Detection and release of deadlocks in the operating system [J]. Journal of Zhengzhou Institute of Light Industry, 1993,8 (3): 53-55.

[3] Dong Jian, Yang Xiaozong, Cheng Xin, et al. fault-tolerant general deadlock model detection algorithm for distributed system [J]. Computer Research and Development, 2007,44 (5): 798-805.

[4] Li Tao, Zhao Hongsheng. Pway optimization of mobile robots based on the evolutionary ant colony algorithm [J]. Control and Decision-making, 2023,38 (3): 612-620.

[5] Xu Jihua, Zhu Xiaojuan. A collaborative decision algorithm based on multi-agent reinforcement learning [Z]. Journal of Ningxia Normal University, 2023,44 (4): 71-79.