动态自校正关键性能指标异常检测算法

IF 3 3区 材料科学 Q2 MATERIALS SCIENCE, CHARACTERIZATION & TESTING Nondestructive Testing and Evaluation Pub Date : 2023-11-09 DOI:10.1080/10589759.2023.2273998
Yufang Sun, Shanghua Gao, Hongxiu Lin, Fenglin Liu, Bin Xing, Bing Guo
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引用次数: 0

摘要

摘要后台系统的运维一直是保证系统高可用性的重要环节。随着后台系统数量的不断增加,其运维也必须从最初的海量策略向智能化方向发展。智能运维的关键是关键性能指标(KPI)的异常检测,如CPU利用率。然而,现有的KPI异常检测算法不仅不能在非参数方法下选择动态阈值,而且没有对虚警进行误报校正的假正校正机制。为了克服上述缺点,本文提出了一种动态自校正关键绩效指标(KPI)异常检测算法,以下简称DSCAD。据我们所知,在KPI异常检测领域,DSCAD算法是第一个不依赖于正态分布假设的动态阈值算法。与现有KPI异常检测方法相比,DSCAD算法的f值提高了3%,具有最佳性能。关键词:KPI异常检测动态阈值选择误报校正披露声明作者未报告潜在利益冲突。本研究得到国家自然科学基金项目(62072319)的部分资助;四川省科技计划项目2023YFQ0022和2022YFG0041;泸州市科技创新研发计划(2022CDLZ-6)
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Dynamic self-correcting key performance indicator anomaly detection algorithm
ABSTRACTThe operation and maintenance of the background system is always an important link to ensure the system’s high availability. With the increasing number of background systems, their operation, and maintenance have to develop from the initial huge-crowd strategy to the direction of intelligence. The key to intelligent operation and maintenance is the abnormal detection of key performance indicators (KPI), such as CPU utilisation. However, the existing KPI anomaly detection algorithms not only cannot select the dynamic threshold under the non-parametric methods but also have no false-positive correction mechanism to correct the false alarms. In order to overcome the above shortcomings, this work proposes a dynamic self-correcting Key Performance Indicator (KPI) anomaly detection algorithm, hereafter referred to as DSCAD. To the best of our knowledge, in the field of KPI anomaly detection, the DSCAD algorithm is the first dynamic threshold algorithm that does not rely on the assumption of normal distribution. Compared with the existing KPI anomaly detection methods, the F-score of the DSCAD algorithm increased by 3% and had the best performance.KEYWORDS: KPI anomaly detectiondynamic threshold selectionfalse-positive correction Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThis work was supported in part by the National Natural Science Foundation of China under Grant 62072319; the Sichuan Science and Technology Program under Grant 2023YFQ0022 and 2022YFG0041; the Luzhou Science and Technology Innovation R&D Program (No. 2022CDLZ-6)
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来源期刊
Nondestructive Testing and Evaluation
Nondestructive Testing and Evaluation 工程技术-材料科学:表征与测试
CiteScore
4.30
自引率
11.50%
发文量
57
审稿时长
4 months
期刊介绍: Nondestructive Testing and Evaluation publishes the results of research and development in the underlying theory, novel techniques and applications of nondestructive testing and evaluation in the form of letters, original papers and review articles. Articles concerning both the investigation of physical processes and the development of mechanical processes and techniques are welcomed. Studies of conventional techniques, including radiography, ultrasound, eddy currents, magnetic properties and magnetic particle inspection, thermal imaging and dye penetrant, will be considered in addition to more advanced approaches using, for example, lasers, squid magnetometers, interferometers, synchrotron and neutron beams and Compton scattering. Work on the development of conventional and novel transducers is particularly welcomed. In addition, articles are invited on general aspects of nondestructive testing and evaluation in education, training, validation and links with engineering.
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