Research on the Reliability of Intelligent Dispatching Management System for Open-pit Mines

Reliability is an important indicator of whether a system can operate stably and successfully, high reliability means that the system can operate normally most of the time, and low reliability is the opposite. The intelligent scheduling system for open-pit mines is usually deployed on the cloud plat...

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Main Authors: QIU Zixian, XU Yueyun, WANG Xiaowei, HU Manjiang, QIN Hongmao
Format: Article
Language:zho
Published: Editorial Office of Control and Information Technology 2022-10-01
Series:Kongzhi Yu Xinxi Jishu
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Online Access:http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2022.05.018
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author QIU Zixian
XU Yueyun
WANG Xiaowei
HU Manjiang
QIN Hongmao
author_facet QIU Zixian
XU Yueyun
WANG Xiaowei
HU Manjiang
QIN Hongmao
author_sort QIU Zixian
collection DOAJ
description Reliability is an important indicator of whether a system can operate stably and successfully, high reliability means that the system can operate normally most of the time, and low reliability is the opposite. The intelligent scheduling system for open-pit mines is usually deployed on the cloud platform, and has a complex structure and a variety of deployment methods, so it is difficult to obtain its reliability information. To this end, this paper proposes a reliability evaluation method based on Monte Carlo simulation, which compares the scheduling system to a hybrid cloud application, and models the scheduling system deployment stack as a hierarchical dependency diagram considering relatively comprehensive components and their dependencies, simulates each component through the Monte Carlo method, and then determines the status of the scheduling system according to the hierarchical dependency diagram. At the same time, considering that different service instance deployment methods have a key impact on the reliability of the scheduling system, this paper proposes a step-by-step traversal algorithm to obtain the best service instance deployment method, and then obtain the most reliable scheduling system deployment method. Experimental results show that the service instance deployment methods proposed in this paper are better than the random deployment method and the uniform deployment method under different ECS selections. Taking the public cloud scheduling system as an example, after traversal deployment, when the total service instance is 40, it can obtain more than 99.99% reliability. The random deployment method requires the total service instance to reach 47 to achieve more than 99.99% reliability; and when the total resources are small, the traversal deployed scheduling system can be up to 3.87% higher than the randomly deployed scheduling system.
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spelling doaj-art-d696d1db47f0440eb7670524645a02f12025-08-25T06:49:02ZzhoEditorial Office of Control and Information TechnologyKongzhi Yu Xinxi Jishu2096-54272022-10-0112212932310153Research on the Reliability of Intelligent Dispatching Management System for Open-pit MinesQIU ZixianXU YueyunWANG XiaoweiHU ManjiangQIN HongmaoReliability is an important indicator of whether a system can operate stably and successfully, high reliability means that the system can operate normally most of the time, and low reliability is the opposite. The intelligent scheduling system for open-pit mines is usually deployed on the cloud platform, and has a complex structure and a variety of deployment methods, so it is difficult to obtain its reliability information. To this end, this paper proposes a reliability evaluation method based on Monte Carlo simulation, which compares the scheduling system to a hybrid cloud application, and models the scheduling system deployment stack as a hierarchical dependency diagram considering relatively comprehensive components and their dependencies, simulates each component through the Monte Carlo method, and then determines the status of the scheduling system according to the hierarchical dependency diagram. At the same time, considering that different service instance deployment methods have a key impact on the reliability of the scheduling system, this paper proposes a step-by-step traversal algorithm to obtain the best service instance deployment method, and then obtain the most reliable scheduling system deployment method. Experimental results show that the service instance deployment methods proposed in this paper are better than the random deployment method and the uniform deployment method under different ECS selections. Taking the public cloud scheduling system as an example, after traversal deployment, when the total service instance is 40, it can obtain more than 99.99% reliability. The random deployment method requires the total service instance to reach 47 to achieve more than 99.99% reliability; and when the total resources are small, the traversal deployed scheduling system can be up to 3.87% higher than the randomly deployed scheduling system.http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2022.05.018intelligent scheduling systemcloud applicationsreliability assessmentMonte Carlo simulationdeployment of server instanceopen-pit mines
spellingShingle QIU Zixian
XU Yueyun
WANG Xiaowei
HU Manjiang
QIN Hongmao
Research on the Reliability of Intelligent Dispatching Management System for Open-pit Mines
Kongzhi Yu Xinxi Jishu
intelligent scheduling system
cloud applications
reliability assessment
Monte Carlo simulation
deployment of server instance
open-pit mines
title Research on the Reliability of Intelligent Dispatching Management System for Open-pit Mines
title_full Research on the Reliability of Intelligent Dispatching Management System for Open-pit Mines
title_fullStr Research on the Reliability of Intelligent Dispatching Management System for Open-pit Mines
title_full_unstemmed Research on the Reliability of Intelligent Dispatching Management System for Open-pit Mines
title_short Research on the Reliability of Intelligent Dispatching Management System for Open-pit Mines
title_sort research on the reliability of intelligent dispatching management system for open pit mines
topic intelligent scheduling system
cloud applications
reliability assessment
Monte Carlo simulation
deployment of server instance
open-pit mines
url http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2022.05.018
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AT wangxiaowei researchonthereliabilityofintelligentdispatchingmanagementsystemforopenpitmines
AT humanjiang researchonthereliabilityofintelligentdispatchingmanagementsystemforopenpitmines
AT qinhongmao researchonthereliabilityofintelligentdispatchingmanagementsystemforopenpitmines