基于CVAE-LSTM的服务器KPI异常检测

doi:10.12305/j.issn.1001-506X.2025.03.34

Abstract

Abstract:

The anomaly detection of key performance indicator (KPI) is the basis of all aspects of Internet intelligent operation and maintenance, and is of great significance for fault alarm and server security. The depth generation model has been able to solve the problem of poor depth feature representation ability of machine learning model, but it is insufficient in terms of the processing of time information in KPI data and the capture of long-term information. For this reason, a KPI anomaly detection model based on the combination of conditional variational autoencoder (CVAE) and long-short term memory (LSTM) is proposed. With the powerful representation ability of CVAE network, time information is added to deep autoencoder, and the long-term memory ability of LSTM is used to improve the long-term anomaly learning and processing ability of the proposed model. The trained CVAE network is used to further train LSTM. Through the comparison experiment with other deep learning models on three open datasets, the experimental results show that the performance of the model in this paper is better than that of the LSTM alone and some deep learning models with better results in terms of F1 value.

Key words: key performance indicator (KPI) anomaly detection, conditional variational autoencoder (CVAE), long-short term memory (LSTM) network, KPI, deep learning

CLC Number:

Xiarun SHEN, Ruonan LI, Haotian ZHANG. Server KPI anomaly detection based on CVAE-LSTM[J]. Systems Engineering and Electronics, 2025, 47(3): 1019-1027.

Figures/Tables 9

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References 27

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数据集名称	数据点数	缺失值点数	异常数据点数
A	129 035	2 754	7 666
B	128 854	2 941	9 581
C	147 669	28	2 945

参数名称	参数意义	参数值
optimizer	优化器	Adam
learning_rate	学习率	0.001
gamma	学习率调整stepLR的惩罚因子	0.75
CVAE_epochs	CVAE网络训练轮数	20
LSTM_epochs	LSTM网络训练轮数	50
latent_dims	隐藏层维度	8

模块名称	层操作名称	参数设置	输出维度	上一层操作
Encoder	input(InputLayer)	-	(batch, 139)	-
	linear_1(Linear)	(139, 100, bias=True)	(batch, 100)	input
	activation_1(ReLU)	-	(batch, 100)	linear_1
	linear_2(Linear)	(100, 100, bias=True)	(batch, 100)	activation_1
	activation_2(ReLU)	-	(batch, 100)	linear_2
	linear_3(Linear)	(100, 8, bias=True)	(batch, 8)	activation_2
	linear_4(Linear)	(100, 8, bias=True)	(batch, 8)	activation_2
	activation_3(Softplus)	beta=1, threshold=20	(batch, 8)	linear_4
LSTM	lstm_1(LSTM)	(8, 64, batch_first=True)	(batch, k, 64)	linear_3
LSTM	lstm_2(LSTM)	(64, 8, batch_first=True)	(batch, k, 8)	lstm_1
Decoder	linear_5(Linear)	(99, 100, bias=True)	(batch, 99)	lstm_2
	activation_4(ReLU)	-	(batch, 100)	linear_5
	linear_6(Linear)	(100, 100, bias=True)	(batch, 100)	activation_4
	activation_5(ReLU)	-	(batch, 100)	linear_6
	linear_7(Linear)	(100, 48, bias=True)	(batch, 48)	activation_5
	linear_8(Linear)	(100, 48, bias=True)	(batch, 48)	activation_5
	activation_6(Softplus)	beta=1, threshold=20	(batch, 48)	linear_8

数据集	性能	LSTM-AD	VAE	CVAE	PUAD	本文模型
A	Precision	78.67	94.19	92.57	97.39	100.00
	Recall	82.31	82.71	91.78	92.12	92.35
	F1	80.44	88.08	92.17	94.68	96.02
B	Precision	58.25	91.96	93.51	95.67	99.91
	Recall	54.27	100.00	100.00	99.94	99.05
	F1	56.19	95.81	96.65	97.76	99.47
C	Precision	72.00	99.21	97.91	96.42	98.32
	Recall	69.83	79.09	88.36	88.10	88.36
	F1	70.90	88.02	92.89	92.07	93.07

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Server KPI anomaly detection based on CVAE-LSTM

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