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Task Scheduling Algorithm of Administrative Management System Based on Ant Colony Optimization Neural Network | IEEE Conference Publication | IEEE Xplore

Task Scheduling Algorithm of Administrative Management System Based on Ant Colony Optimization Neural Network


Abstract:

In order to better solve the problem of large amount of interactive data in cloud computing task scheduling in administrative system, a hybrid genetic annealing algorithm...Show More

Abstract:

In order to better solve the problem of large amount of interactive data in cloud computing task scheduling in administrative system, a hybrid genetic annealing algorithm is proposed based on the advantages of genetic algorithm and annealing algorithm. According to the characteristics of the task scheduling data relationship in the administrative system, the algorithm encodes the execution task in the cloud computing environment, searches the global optimal solution of the task data through the fitness function of the genetic algorithm, and uses the annealing algorithm to adjust the fitness function to speed up the completion time of the task scheduling in the system virtual machine during the coding and cross reorganization of the task data. Compared with genetic algorithm, simulated annealing algorithm and BP algorithm with momentum term, the simulation results show that the neural network trained by ACO algorithm has a faster convergence speed and can reach a smaller mean square error. The system realizes the free customization of assessment indicators, the flexible setting of assessment indicators’ weights and assessment scores, and the storage, tracking and management of assessment results over the years. Scheduling is a NP-hard problem, and many heuristic methods at present still have defects in optimization ability. With the development and maturity of artificial intelligence, one of the important branches, the theory of computational intelligence, has been widely used. The dial test system simulates the behavior of terminal users, executes dial test services, and obtains service usage by analyzing service performance indicators that users care about, and provides services for terminal service testing of mobile networks. This paper mainly studies the task resource scheduling model of the dial test system, and proposes an autonomous task scheduling algorithm suitable for the dial test system environment.
Date of Conference: 26-28 May 2023
Date Added to IEEE Xplore: 17 July 2023
ISBN Information:
Conference Location: Changchun, China
References is not available for this document.

I. Introduction

With the expansion of mobile network scale, the increasingly fierce competition in the mobile communication market, and the continuous improvement of users’ requirements for network quality, keeping the network quality ahead is the foundation for the rapid development of enterprises [1]. Most of the existing network quality measurements are aimed at the system, but the service experience of users is not taken into account. Therefore, sometimes the quality of the network is stable but the service experience of users is poor [2]. However, different user libraries and user authentication systems are formed due to different development technical standards and functional points of application systems. It is difficult for information systems to share data with each other and become isolated islands of information instead of being combined into an organic whole, which reduces the utilization rate of information resources [3]. The performance appraisal of administrative personnel is an important basis for personnel decision-making (such as promotion, promotion, reward and punishment, retention or dismissal) in the university system. However, compared with the increasingly strengthened and gradually perfected performance appraisal of college teachers, the performance appraisal of administrative personnel is relatively lagging behind [4].

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1.
Tang Yuexia, "Research on task scheduling algorithm for administrative management system", Electronic Design Engineering, no. 6, pp. 5, 2017.
2.
Li Gang and Wu Zhijun, "Task Scheduling Algorithm for Wide Area Information Management System Based on Multiple QoS Constraints", Journal of Communications, vol. 040, no. 007, pp. 27-37, 2019.
3.
Zhang Juncai, "Anti-conflict task scheduling method for massive dynamic information management system", Journal of Inner Mongolia University for Nationalities: Natural Science Edition, vol. 34, no. 2, pp. 5, 2019.
4.
Wang Bo and Xu Jing, "Research on RPROP hybrid algorithm simulation of BP neural network based on ant colony optimization algorithm", Computer Measurement and Control, vol. 026, no. 007, pp. 195-197, 2018.
5.
Ge Junwei, Guo Qiang and Fang Yiqiu, "A Multi-objective Optimization Cloud Computing Task Scheduling Strategy Based on Improved Ant Colony Algorithm", Microelectronics and Computer, vol. 34, no. 11, pp. 5, 2017.
6.
Jia Liyun, Zhang Xiangli and Zhang Hongmei, "Heuristic Task Scheduling Algorithms in Distributed Systems", Computer Engineering and Applications, vol. 53, no. 12, pp. 7, 2017.
7.
Yu Guolong, Cui Zhongwei, Xiong Weicheng et al., "Research on cloud platform task scheduling based on optimized DPSO algorithm", Journal of Inner Mongolia Normal University (Chinese version of natural science), vol. 048, no. 004, pp. 357-361, 2019.
8.
Wang Ying and Chen Xinpeng, "Research on task scheduling algorithm based on cluster computing", Modern Computer, no. 9, pp. 4, 2020.
9.
Li Xin and Bai Xingwu, "Optimization of task scheduling algorithm for embedded real-time operating system based on Linux", Automation and Instrumentation, no. 9, pp. 4, 2020.
10.
Kang Yaqiong, "Design of Informationized Integrated Operation and Maintenance Administrative Management System", Automation Technology and Application, vol. 41, no. 3, pp. 6, 2022.
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References

References is not available for this document.