School of Information Engineering,Jiaxing Nanhu University;Os-Easy Group Holding Ltd;Colledge of Information Science and Engineering,Jiaxing University;
[Objective] Cloud desktop laboratories are essential in supporting experimental teaching in colleges and universities. However,managing computer hardware faults in large-scale cloud desktop laboratories remains a challenge due to the high number of terminal faults.Existing fault diagnosis methods are often inefficient and fail to provide timely prewarning for hardware faults(physical faults), such asissues with CPU, memory, motherboards, hard drives, and power supplies. To address the uncertainty of downtime caused by hardwarefaults in such environments, an intelligent prewarning model for computer hardware faults in cloud desktop terminals is proposed, utilizingcloud desktop technology and machine learning algorithms. [Methods] This study leverages a hardware status perception system designedfor cloud desktop laboratories with VDI, VOI, and IDV fusion architecture. The perception system collects data from terminal computers,including cumulative usage time, utilization rates, energy consumption, usage frequency, repair history, hardware changes, and variouswarnings such as CPU high temperature or load, memory high load, disk IO high load, insufficient hard disk space, graphics card hightemperature, abnormal crashes or blue screens, and network issues. Furthermore, the system records whether hardware faults haveoccurred. The collected data is used to train and evaluate the intelligent prewarning model with machine learning algorithms, includingKNN, decision tree, support vector machine, and XGBoost. The data set, sourced from real production environments provided by a cloudcomputing company, undergoes transformation, cleaning, and normalization to enhance training accuracy. The data size is reduced from9 850 × 18 dimensions to 9 630 × 15 dimensions. The training set and test set account for 80% and 20% of the total data, respectively. Thetraining set includes 7 704 samples(6 576 nonfault data and 1 128 fault data), while the test set contains 1 926 samples(1 644 nonfaultdata and 282 fault data). To evaluate the robustness and generalization ability of the prewarning model, the positive and negativeproportions of the data samples are adjusted, and the performance indicators, namely precision, recall, F1-score, accuracy, and AUC, arecalculated for all four models. [Results] Experimental results show that the XGBoost-based prewarning model demonstrates superiorrobustness and generalization compared to other models. [Conclusions] This intelligent prewarning model for large-scale cloud desktoplaboratory terminal computer hardware faults, built on machine learning, offers significant economic and practical benefits. It notablyimproves fault prewarning accuracy, reduces costs related to extensive computer updates, strengthens the refined management oflaboratories, and advances the overall level of laboratory construction management.
| 148 | 0 | 0 |
| Downloads | Citas | Reads |
[1]胡继华,陈薇薇,伍丽华.面向实验教学和学生创新活动的复合云桌面设计与构建[J].实验技术与管理, 2020, 37(6):246–262.HU J H, CHEN W W, WU L H. Design and construction of composite cloud desktop forexperimental teaching and student innovation activities[J]. Experimental Technology and Management, 2020, 37(6):246–262.(in Chinese)
[2]路遥,范雪松,崔建.计算机实验教学系统建设与管理方案研究[J].实验室研究与探索, 2020, 39(3):142–145.LU Y, FAN X S, GUI J. Research on the construction and management scheme of computer experimental teaching system[J]. Research and Exploration in Laboratory, 2020, 39(3):142–145.(in Chinese)
[3]胡国强,杨彦荣.智慧教育背景下高校智慧实验室的构建与研究[J].实验技术与管理, 2021, 38(3):283–287.HU G Q, YANG Y R. Construction and research of university intelligent laboratory under context of intelligent education[J].Experimental Technology and Management, 2021, 38(3):283–287.(in Chinese)
[4]韩友前.基于IDV技术的智能云教室部署与优化[J].实验技术与管理, 2021, 38(2):237–245.HAN Y Q. Deployment and optimization of intelligent cloud classroom based on IDV technology[J]. Experimental Technology and Management, 2021, 38(2):237–245.(in Chinese)
[5]李轶.基于VDI、VOI融合的云桌面在高校实验室中的应用[J].江汉大学学报（自然科学版）, 2023, 51(6):72–79.LI Y. A cloud desktop practice based on fusion of VDl and VOl in university laboratory[J]. Journal of Jianghan University(Natural Science Edition), 2023, 51(6):72–79.(in Chinese)
[6]谢秀俤,赵火平,林清强.地方高校科研实验室效率提升机制研究[J].实验技术与管理, 2021, 38(3):258–261, 287.XIE X D, ZHAO H P, LIN Q Q. Research on mechanism of improving efficiency of scientific research laboratories in local universities[J]. Experimental Technology and Management, 2021,38(3):258–261, 287.(in Chinese)
[7]杨楠,赵骥,郑红英,等.基于VOI模式的高校多媒体教室桌面云的应用与分析[J].华北理工大学学报（社会科学版）,2021, 21(6):100–105.YANG N, ZHAO J, ZHENG H Y, et al. Analysis and application of the desktop cloud in multimedia instruction environment based on the VOI model[J]. Journal of North China University of Science and Technology(Social Science Edition), 2021,21(6):100–105.(in Chinese)
[8]苗桂君,尤枫,刘锦华,等.基于IDV技术的高校公共机房桌面虚拟化平台研究与实践[J].实验技术与管理, 2019, 36(12):267–270, 277.MIAO G J, YOU F, LIU J H, et al. Research and practice of desktop virtualization platform for university public computer room based on IDV technology[J]. Experimental Technology and Management, 2019, 36(12):267–270, 277.(in Chinese)
[9]罗士美,沈克农.基于KVM和OpenStack融合桌面云实验平台的设计与实现[J].山东农业大学学报（自然科学版）, 2020,51(2):269–272.LUO S M, SHEN K N. Design and implementation of an integration desktop cloud experiment platform based on KVM and Open Stack[J]. Journal of Shandong Agricultural University(Natural Science Edition), 2020, 51(2):269–272.(in Chinese)
[10] ZHANG D, XIE Y, XU M, et al. Troy:Efficient service deployment for windows systems[J]. Chinese Journal of Electronics, 2024,33(1):313–322.
[11]吴湘宁,罗勋鹤,刘远兴,等.高校云桌面计算机实验室方案选择与设计[J].实验技术与管理, 2020, 37(6):20–23.WU X N, LUO X H, LIU Y X, et al. Selection and design scheme of cloud desktop computer laboratory in colleges and universities[J]. Experimental Technology and Management,2020, 37(6):20–23.(in Chinese)
[12]杨剑锋,乔佩蕊,李永梅,等.机器学习分类问题及算法研究综述[J].统计与决策, 2019, 35(6):36–40.YANG J F, QIAO P R, LI Y M, et al. A Review of machinelearning classification and algorithms[J]. Statistics&Decision,2019, 35(6):36–40.(in Chinese)
[13] GUPTA R. A survey on machine learning approaches and its techniques[C]//In 2020 IEEE International Students’ Conference on Electrical, Electronics and Computer Science(SCEECS).IEEE, 2020:1–6.
[14] RANI V, NABI S T, KUMAR M, et al. Self-supervised learning:A succinct review[J]. Archives of Computational Methods in Engineering, 2023, 30(4):2761–75.
[15] ALNUAIMI A F A H, ALBALDAWI T H K. An overview of machine learning classification techniques[J]. BIO Web of Conferences, 2024(97):1–24.
[16] SINGH V, GANGSAR P, PORWAL R, et al. Artificial intelligence application in fault diagnostics of rotating industrial machines:A state-of-the-art review[J]. Journal of Intelligent Manufacturing,2023, 34(3):931–960.
[17] PERETZ, O, MICHAL K, ODED K. Naive Bayes classifier:An ensemble procedure for recall and precision enrichment[J].Engineering Applications of Artificial Intelligence, 2024(136):108972.
[18] CUNNINGHAM P, DELANY S J. K-nearest neighbour classifiers-a tutorial[J]. ACM computing surveys(CSUR), 2021, 54(6):1–25.
[19] ZHOU H, ZHANG J, ZHOU Y, et al. A feature selection algorithm of decision tree based on feature weight[J]. Expert Systems with Applications, 2021(164):113842.
[20] CERVANTES J, GARCIA-LAMONT F, RODRíGUEZMAZAHUA L, et al. A comprehensive survey on support vector machine classification:Applications, challenges and trends[J].Neurocomputing, 2020(408):189–215.
[21] ROSHAN S E, ASADI S. Improvement of bagging performance for classification of imbalanced datasets using evolutionary multi-objective optimization[J]. Engineering Applications of Artificial Intelligence, 2020(87):103319.
[22] MENSI A, BICEGO M. A novel anomaly score for isolation forests[C]//Image Analysis and Processing–ICIAP 2019:20th International Conference. Trento, Italy:Springer International Publishing, 2019:152–163.
[23] ISHWARAN, HEMANT, et al. Random survival forests[J]. The Annals of Applied Statistics, 2008, 2(3):841–860.
[24] GONZáLEZ S, GARCíA S, DEL S J, et al. A practical tutorial on bagging and boosting based ensembles for machine learning:Algorithms, software tools, performance study, practical perspectives and opportunities[J]. Information Fusion, 2020(64):205–237.
[25] WANG Y, FENG L. An adaptive boosting algorithm based on weighted feature selection and category classification confidence[J]. Applied Intelligence, 2021, 51(10):6837–6858.
[26] BENTéJAC C, CS?RG?A, MARTíNez-MU?OZ G. A comparative analysis of gradient boosting algorithms[J]. Artificial Intelligence Review, 2021(54):1937–1967.
[27] HOANG, ANH T, et al. A review on application of artificial neural network(ANN)for performance and emission characteristics of diesel engine fueled with biodiesel-based fuels[J]. Sustainable Energy Technologies and Assessments, 2021(47):101416.
[28] SARKER I H. Machine learning:Algorithms, real-world applications and research directions[J]. SN Computer Science, 2021, 2(3):160.
[29] TAUNK K, DE S, VERMA S, et al. A brief review of nearest neighbor algorithm for learning and classification[C]//In 2019International Conference on Intelligent Computing and Control Systems(ICCS). IEEE, 2019:1255–1260.
[30] JEN L, LIN Y H. A brief overview of the accuracy of classification algorithms for data prediction in machine learning applications[J]. Journal of Applied Data Sciences, 2021, 2(3):84–92.
[31] ABDULLAH D M, ABDULAZEEZ A M. Machine learning applications based on SVM classification a review[J]. Qubahan Academic Journal, 2021, 1(2):81–90.
[32] KUMARI P, KAUR P. A survey of fault tolerance in cloud computing[J]. Journal of King Saud University-Computer and Information Sciences, 2021, 33(10):1159–76.
[33] LUENGO J, GARCíA-GIL D, RAMíRez-GALLEGO S, et al.Big data preprocessing[M]. Cham:Springer, 2020:4–9.
Basic Information:
DOI：10.16791/j.cnki.sjg.2025.04.028
China Classification Code:TP181;TP393.09
Citation Information:
[1]孙彦武,张陈登,张丽华等.基于机器学习的大规模云桌面实验室计算机故障预警模型构建研究[J].实验技术与管理,2025,42(04):220-226.DOI:10.16791/j.cnki.sjg.2025.04.028.
Fund Information:
教育部产学合作协同育人项目(220801381261347)