自适应RAN性能异常检测

Dovile Momkute, Karolis Žvinys, V. Barzdenas
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引用次数: 1

摘要

移动无线接入网络的复杂性正在迅速增加。与此同时,客户对服务质量的期望也给运营商带来了压力。维持一个可持续的、高性能的无线接入网需要巨大的人力投入。为了满足最高的质量和服务水平要求,越来越多的网络管理和维护功能需要传递给自动化解决方案。现在已经有很多了,比如SON、MLB、自动天线倾斜,这有助于扩大运营规模。它们致力于改善特定的功能,但不太关注服务kpi,而服务kpi是客户体验的直接反映。大多数情况下,网络性能kpi的变化表明服务质量出现中断或下降,需要尽快恢复。基于机器学习过程的自动化解决方案使工程师能够更快地注意到这些网络问题。有很多算法是为了这个目的而创建的,但它们中的大多数都是专门用于检测特殊数据集中的异常,比如欺诈、异常的CPU使用、地震、网络攻击。因此,在识别电信网络性能数据异常时,它要么不适用,要么表现不佳。本文的研究证明了现有R库对电信数据异常检测的准确性、功能不足等问题。唯一的R“changepoint”包不仅具有检测单个本地异常的功能,还具有检测异常时间序列的功能,这与RAN(无线接入网)性能监控最相关。方法- PELT,惩罚- BIC的默认函数meanvar()在可保留性和完整性KPI中返回多达30个假阳性异常,在一个月时间序列数据的流动性KPI组中返回多达10个假阳性异常。为了提取真阳性异常,应用了额外的算法,在单个时间序列数据中,假阴性检测的成本增加了10%,并且在使用的数据集中发生的概率不超过5%。这些结果与其他流行的R库,如“Anomalize”,“AnomalyDetection”,“bcp”,“changepoint”进行了比较,并被证明是最准确的无线网络性能数据。这种经过调整的异常检测算法可以被视为一种可扩展的通用算法,适用于任何类型的高维数据,因为它不需要针对不同的KPI组或技术进行额外调优。它是一种自动化网络性能监控的解决方案,在解决网络性能问题的同时,可以减轻网络工程师的日常工作负担,提高质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adapted Anomaly Detection for RAN Performance
Complexity of mobile radio access networks is increasing rapidly. At the same time, customers’ expectations for the service quality are keeping the pressure on operators. It requires huge human efforts to maintain a sustainable and high- performance radio access network. To meet the highest quality and service level requirements, more and more network management and maintenance functions are required to be passed to automated solutions. There are many already - like SON, MLB, automatic antenna tilting which helps to scale operations. Those are dedicated to improving specific functions, but they do not pay much attention to service KPIs, which are a direct reflection of customers’ experience. Most often, changes in network performance KPIs indicate some interruption or deterioration of service quality, which needs to be restored as soon as possible. Automated solutions based on machine learning process enables engineers to notice those network issues much faster. There are many algorithms that have been created for this purpose, but most of them are specialized and tuned to detect an anomaly in the special datasets, like fraud, abnormal CPU usage, earthquake, cyber-attack. Consequently, it is either not applicable or not performing well while identifying anomalies on telecommunication network performance data. The research on this paper proofs anomaly detection accuracy problem, lack of functions for telecommunication data in existing R libraries. The only R “changepoint” package has a functionality to detect not a single local anomaly, but also anomalous time series, which is the most relevant in RAN (radio access network) performance monitoring. Default function meanvar() of method – PELT, penalty - BIC returns up to 30 false positive anomalies in retainability and integrity KPI and up to 10 in mobility KPI group on one month time series data. To extract true positive anomalies additional algorithm is applied with a cost of false negative detection increase up to 10% in a single time series data and occurs not more than in 5% of used datasets. These results are compared against other popular R libraries, like “Anomalize”, “AnomalyDetection”, “bcp”, “changepoint” and proofed to be the most accurate on radio network performance data. This adapted anomaly detection algorithm can be treated as a scalable generic algorithm to any kind of high-dimensionality data as it does not require additional tuning for different KPI group or technology. It is the solution for automated network performance monitoring that can take off a lot of workload from daily network engineer routine and improve the quality while tackling network performance issues.
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