基于时序数据的云网协同平台人工智能运维体系

doi:10.11959/j.issn.1000-0801.2022290

Abstract

Abstract:

The expansion of cloud computing in enterprise applications is not only the expansion of platform scale, but also the extension of platform applications.“Cloud-network collaboration” and “micro-service” are important trends in the evolution of enterprise cloud platforms.As the importance of enterprise information construction continues to rise, the operation and maintenance of the microservice-oriented cloud-network collaboration platform faces great challenges.The challenges faced by the operation and maintenance were analyzed, the requirements for artificial intelligence operation and maintenance of the platform were sorted out, a technical system for artificial intelligence operation and maintenance of the platform based on time-series data analysis was proposed, and an artificial intelligence operation and maintenance subsystem for the cloud network collaborative platform was designed.The proposed technical system and system design are practical and generalizable, and can be used as a reference for the technical approach of enterprise cloud platform construction and optimization.

Key words: cloud platform, cloud network collaboration, micro-service, time series data, artificial intelligence operation and maintenance

CLC Number:

TP393

Ruiying CHENG, Pan ZHANG, Yu XIAO, Yujie QIAO, Anyi ZHANG. Time series data based AI operation and maintenance system of cloud network collaboration platform[J]. Telecommunications Science, 2022, 38(11): 24-35.

Figures/Tables 4

References 23

[24]	ZHANG G C , CHEN L , LIANG K K . Fault detection and diagnosis for aerostat sensors based on PCA and contribution graph[C]// Proceedings of 2019 IEEE 3rd Information Technology,Networking,Electronic and Automation Control Conference. Piscataway:IEEE Press, 2019: 224-228.
[25]	李泰, 韩强, 黄银龙 ,等. 基于 kNN 算法的红外测温图谱的温度数字识别研究[J]. 电力信息与通信技术, 2019,17(6): 14-19.
	LI T , HAN Q , HUANG Y L ,et al. Research on digital recognition of infrared temperature map based on kNN algorithm[J]. Electric Power Information and Communication Technology, 2019,17(6): 14-19.
[26]	ANGIULLI F , FASSETTI F . Distance-based outlier queries in data streams:the novel task and algorithms[J]. Data Mining and Knowledge Discovery, 2010,20(2): 290-324.
[27]	LIU J , DENG H F . Outlier detection on uncertain data based on local information[J]. Knowledge-Based Systems, 2013(51): 60-71.
[28]	姜红红, 张涛, 赵新建 ,等. 基于大数据的电力信息网络流量异常检测机制[J]. 电信科学, 2017,33(3): 134-141.
	JIANG H H , ZHANG T , ZHAO X J ,et al. A big data based flow anomaly detection mechanism of electric power information network[J]. Telecommunications Science, 2017,33(3): 134-141.
[29]	JULISCH K . Clustering intrusion detection alarms to support root cause analysis[J]. ACM Transactions on Information and System Security, 2003,6(4): 443-471.
[30]	BENNACER L , CIAVAGLIA L , GHAMRI-Doudane S ,et al. Scalable and fast root cause analysis using inter cluster inference[C]// Proceedings of 2013 IEEE International Conference on Communications (ICC). Piscataway:IEEE Press, 2013: 3563-3568.
[31]	STOJANOVIC L , DINIC M , STOJANOVIC N ,et al. Big-data-driven anomaly detection in industry (4.0):An approach and a case study[C]// Proceedings of 2016 IEEE International Conference on Big Data (Big Data). Piscataway:IEEE Press, 2016: 1647-1652.
[32]	ZHOU X K , HU Y Y , LIANG W ,et al. Variational LSTM enhanced anomaly detection for industrial big data[J]. IEEE Transactions on Industrial Informatics, 2021,17(5): 3469-3477.
[33]	DONG L , LIU S L , ZHANG H L . A method of anomaly detection and fault diagnosis with online adaptive learning under small training samples[J]. Pattern Recognition, 2017(64): 374-385.
[34]	Cloud developer center launched by Zenoss[EB]. Telecomworldwire, 2022.
[35]	李晨, 解思江, 郝颖 ,等. 信息系统安全运行自动化手段在电力公司的探索[J]. 电信科学, 2017,33(S1): 123-128.
	LI C , XIE S J , HAO Y ,et al. Study on the automatic maintenance for information system security in power company[J]. Telecommunications Science, 2017,33(S1): 123-128.
[36]	唐颖淳 . 利用 Kafka 实现大话务并发数据流的吞吐系统[J]. 电信科学, 2018,34(S2): 134-139.
[1]	史凡 . 云网络：云网融合的新型网络发展趋势[J]. 中兴通讯技术, 2022,28(1): 8-10.
	SHI F . Cloud network:new network development trend of cloud network convergence[J]. ZTE Technology Journal, 2022,28(1): 8-10.
[2]	宋志刚, 林杰, 王金超 . 基于容器云为云网融合提供全面运维服务的“云网管+”平台[J]. 信息技术与信息化, 2021(2): 118-121.
	SONG Z G , LIN J , WANG J C . “Cloud network management+”platform based on container cloud to provide comprehensive operation and maintenance services for cloud network convergence[J]. Information Technology and Informatization, 2021(2): 118-121.
[3]	官东亮 . 基于微服务的业务平台架构重构[J]. 电信科学, 2020,36(9): 75-83.
	GUAN D L . Service platform architecture reconstruction based on microservices[J]. Telecommunications Science, 2020,36(9): 75-83.
[4]	董娜, 刘伟娜, 侯波涛 . 基于大数据的网络异常行为建模方法[J]. 电力信息与通信技术, 2018,16(1): 6-10.
	DONG N , LIU W N , HOU B T . Modeling method of network abnormal behavior based on big data[J]. Electric Power Information and Communication Technology, 2018,16(1): 6-10.
[5]	任毅华, 万志远, 吕东 . 人工智能技术的变电运维软件设计与研究[J]. 电子世界, 2022(1): 29-30.
	REN Y H , WAN Z Y , LYU D . Design and research of substation operation and maintenance software with artificial intelligence technology[J]. Electronics World, 2022(1): 29-30.
[6]	陈真, 王雅志 . 基于人工智能的运维系统建设研究与应用[J]. 常州工学院学报, 2021,34(3): 35-40.
	CHEN Z , WANG Y Z . Research and application of the construction of operation and maintenance system based on artificial intelligence[J]. Journal of Changzhou Institute of Technology, 2021,34(3): 35-40.
[7]	李朝霞, 刘金春, 邢鑫 . 人工智能在网络运维中的应用[J]. 电子技术与软件工程, 2021(10): 5-6.
	LI Z X , LIU J C , XING X . Artificial intelligence in network operations and maintenance[J]. Electronic Technology ＆ Software Engineering, 2021(10): 5-6.
[8]	HE P J , ZHU J M , ZHENG Z B ,et al. Drain:an online log parsing approach with fixed depth tree[C]// Proceedings of 2017 IEEE International Conference on Web Services. Piscataway:IEEE Press, 2017: 33-40.
[9]	ZHANG S L , MENG W B , BU J H ,et al. Syslog processing for switch failure diagnosis and prediction in datacenter networks[C]// Proceedings of 2017 IEEE/ACM 25th International Symposium on Quality of Service (IWQoS). Piscataway:IEEE Press, 2017: 1-10.
[10]	RAMOS J , . Using TF-IDF to determine word relevance in document queries[C]// Proceedings of the 1st Instructional Conference on Machine Learning.[S.l.:s.n.], 2003: 29-48.
[11]	MIKOLOV T , CHEN K , CORRADO G ,et al. Efficient estimation of word representations in vector space[J]. arXiv preprint,2013,arXiv:1301.3781.
[12]	SIGELMAN B H , BARROSO L A , BURROWS M ,et al. Dapper,a large-scale distributed systems tracing infrastructure[EB]. 2010.
[13]	Zipkin,from Twitter a distributed tracing system[EB]. 2022.
[14]	严丽云, 杨新章, 何震苇 ,等. 基于运营商视角的服务网格技术评测与集成方案[J]. 电信科学, 2020,36(6): 144-153.
	YAN L Y , YANG X Z , HE Z W ,et al. Service mesh technology evaluation and integration scheme based on telecom operator perspective[J]. Telecommunications Science, 2020,36(6): 144-153.
[15]	Buoyant.Linkerd[EB]. 2016.
[16]	SILVA R F , PLIS S M , ADALI T ,et al. Multidataset independent subspace analysis extends independent vector analysis[C]// Proceedings of 2014 IEEE International Conference on Image Processing (ICIP). Piscataway:IEEE Press, 2014: 2864-2868.
[17]	刘立, 朱健成, 韩光洁 ,等. 基于 1D-CNN 联合特征提取的轴承健康监测与故障诊断[J]. 软件学报, 2021,32(8): 2379-2390.
	LIU L , ZHU J C , HAN G J ,et al. Bearing health monitoring and fault diagnosis based on joint feature extraction in 1D-CNN[J]. Journal of Software, 2021,32(8): 2379-2390.
[18]	XING Y Y , YU G X , DOMENICONI C ,et al. Multi-label co-training[C]// Proceedings of the 27th International Joint Conference on Artificial Intelligence. Amsterdam:Elsevier, 2018: 2882-2888.
[19]	杜岳峰, 李晓光, 宋宝燕 . 异构模式中关联数据的一致性规则发现方法[J]. 计算机研究与发展, 2020,57(9): 1939-1948.
	DU Y F , LI X G , SONG B Y . Discovering consistency constraints for associated data on heterogeneous schemas[J]. Journal of Computer Research and Development, 2020,57(9): 1939-1948.
[20]	DINH V , HO L S T . Consistent feature selection for neural networks via Adaptive Group Lasso[EB]. 2020.
[21]	WANG H F , DENG X Y , JIANG W ,et al. A new belief divergence measure for Dempster-Shafer theory based on belief and plausibility function and its application in multi-source data fusion[J]. Engineering Applications of Artificial Intelligence, 2021(97): 104030.
[22]	ZHANG Z , JIANG W , GENG J ,et al. Fault diagnosis based on non-negative sparse constrained deep neural networks and dempster-shafer theory[J]. IEEE Access, 2020(8): 18182-18195.
[36]	TANG Y C . Using Kafka to implement the throughput system of large traffic and concurrent data flow[J]. Telecommunications Science, 2018,34(S2): 134-139.
[23]	WANG X , HE Y L , XU Y ,et al. Comprehensive evaluation modeling and analysis based on ELM integrated AHP and PCA:application to food safety[C]// Proceedings of 2019 Chinese Automation Congress (CAC). Piscataway:IEEE Press, 2019: 4092-4097.

Metrics

Recommended 0

No Suggested Reading articles found!

Time series data based AI operation and maintenance system of cloud network collaboration platform

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 4

References 23

Related Articles 10

Metrics

Recommended 0

[1]	Liyun YAN,Xinzhang YANG,Zhenwei HE,Yuanzhi LIN,Shaoxin HOU. Service mesh technology evaluation and integration scheme based on telecom operator perspective [J]. Telecommunications Science, 2020, 36(6): 144-153.
[2]	Zhimin YANG,Danke HONG,Li WANG,Junyi XIE. Operation support system of power communication network based on cloud platform [J]. Telecommunications Science, 2020, 36(4): 161-169.
[3]	Junwen WANG. Technical requirement of future industrial internet [J]. Telecommunications Science, 2019, 35(8): 26-38.
[4]	Liyun YAN,Xinzhang YANG,Zhenwei HE,Ling ZHANG,Shaoxin HOU. Micro-service solution of operator’s business platform [J]. Telecommunications Science, 2018, 34(11): 166-174.
[5]	Min WEI,Yi WANG. Research and application of remote update technology of IoT terminal [J]. Telecommunications Science, 2018, 34(10): 137-142.
[6]	Junwen LIU,Jiaxing XUAN. Functional requirements and design of simulation cloud platform for data center [J]. Telecommunications Science, 2017, 33(5): 176-182.
[7]	Honglei XU,Yuan ZHANG. Solution for on-demand network in smart campus [J]. Telecommunications Science, 2017, 33(12): 157-163.
[8]	Man Li,Hongming Qiao,Wensheng Yao. Study of IT Internet Target Architecture and Evolution Strategy of Telecom Enterprise [J]. Telecommunications Science, 2014, 30(6): 33-37.
[9]	Guifang Shi,Chenrong Liu. Application Research for Data Acquisition and Summary Statistics System Based on Cloud Platform [J]. Telecommunications Science, 2013, 29(12): 107-112.
[10]	Shasha Wei,Huijuan Lu,Wei Jin,Chao Li. Maximum Mutual Information Feature Extraction Method Based on the Cloud Platform [J]. Telecommunications Science, 2013, 29(10): 38-42.