基于RUL和SVs-GFF的云服務(wù)器老化預測方法

孟海寧; 童新宇; 謝國; 張貝貝; 黑新宏

doi:10.16383/j.aas.c211112

基于RUL和SVs-GFF的云服務(wù)器老化預測方法

doi: 10.16383/j.aas.c211112

孟海寧^{1, 2,},
童新宇^1,,
謝國^1,,
張貝貝^1,,
黑新宏^1,

1.
西安理工大學(xué)計算機科學(xué)與工程學(xué)院西安 710048
2.
陜西省網(wǎng)絡(luò )計算與安全技術(shù)重點(diǎn)實(shí)驗室西安 710048

基金項目: 國家自然科學(xué)基金(61602375, 61773313), 陜西省自然科學(xué)基礎研究計劃基金(2019JQ-749)資助

詳細信息

作者簡(jiǎn)介:
孟海寧：西安理工大學(xué)計算機科學(xué)與工程學(xué)院副教授. 主要研究方向為機器學(xué)習, 故障診斷與預測. 本文通信作者. E-mail: hnmeng@xaut.edu.cn

童新宇：西安理工大學(xué)計算機科學(xué)與工程學(xué)院博士研究生. 主要研究方向為機器學(xué)習, 時(shí)間序列預測. E-mail: tongxinyu@stu.xaut.edu.cn

謝國：西安理工大學(xué)教授. 主要研究方向為數據分析, 故障診斷. E-mail: guoxie@xaut.edu.cn

張貝貝：西安理工大學(xué)計算機科學(xué)與工程學(xué)院講師. 主要研究方向為數據挖掘, 大數據技術(shù). E-mail: bbzhang115@hotmail.com

黑新宏：西安理工大學(xué)計算機科學(xué)與工程學(xué)院教授. 主要研究方向為機器學(xué)習, 系統安全. E-mail: heixinhong@xaut.edu.cn

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- 文章訪(fǎng)問(wèn)數: 353
- HTML全文瀏覽量: 99
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- 被引次數: 0
出版歷程
- 收稿日期: 2021-11-24
- 錄用日期: 2022-04-28
- 網(wǎng)絡(luò )出版日期: 2023-06-29
- 刊出日期: 2024-10-21

Cloud Server Aging Prediction Method Based on RUL and SVs-GFF

1.
School of Computer Science and Engineering, Xi＇an University of Technology, Xi＇an 710048
2.
Shaanxi Key Laboratory Network Computer and Security Technology, Xi＇an 710048

Funds: Supported by National Natural Science Foundation of China (61602375, 61773313) and Natural Science Basic Research Plan of Shaanxi Province (2019JQ-749)

More Information

Author Bio:
MENG Hai-Ning　Associate professor at the School of Computer Science and Engineering, Xi＇an University of Technology. Her research interest covers machine learning and fault prognosis & prediction. Corresponding author of this paper

TONG Xin-Yu　Ph.D. candidate at the School of Computer Science and Engineering, Xi＇an University of Technology. His research interest covers machine learning and time series prediction

XIE Guo　Professor at Xi＇an University of Technology. His research interest covers data analysis and fault diagnosis

ZHANG Bei-Bei　Lecturer at the School of Computer Science and Engineering, Xi＇an University of Technology. His research interest covers data mining and big data technology

HEI Xin-Hong　Professor at the School of Computer Science and Engineering, Xi＇an University of Technology. His research interest covers machine learning and system security

摘要

摘要: 針對云服務(wù)器中存在軟件老化現象, 將造成系統性能衰退與可靠性下降問(wèn)題, 借鑒剩余使用壽命(Remaining useful life, RUL)概念, 提出基于支持向量和高斯函數擬合(Support vectors and Gaussian function fitting, SVs-GFF)的老化預測方法. 首先, 提取云服務(wù)器老化數據的統計特征指標, 并采用支持向量回歸(Support vector regression, SVR)對統計特征指標進(jìn)行數據稀疏化處理, 得到支持向量(Support vectors, SVs)序列數據; 然后, 建立基于密度聚類(lèi)的高斯函數擬合(Gaussian function fitting, GFF)模型, 對不同核函數下的支持向量序列數據進(jìn)行老化曲線(xiàn)擬合, 并采用Fréchet距離優(yōu)化算法選取最優(yōu)老化曲線(xiàn); 最后, 基于最優(yōu)老化曲線(xiàn), 評估系統到達老化閾值前的RUL, 以預測系統何時(shí)發(fā)生老化. 在OpenStack云服務(wù)器4個(gè)老化數據集上的實(shí)驗結果表明, 基于RUL和SVs-GFF的云服務(wù)器老化預測方法與傳統預測方法相比, 具有更高的預測精度和更快的收斂速度.
- 云服務(wù)器 /
- 軟件老化 /
- 支持向量回歸 /
- 高斯函數擬合 /
- 剩余使用壽命
Abstract: Aiming at the problem that software aging in cloud servers will cause system performance degradation and reliability descending, a software aging prediction method based on support vectors (SVs) and Gaussian function fitting (SVs-GFF) with the use of the concept of remaining useful life (RUL) is proposed. Firstly, the statistical characteristic indexes of aging data on a cloud server are extracted, and then support vector regression (SVR) is used to sparse the data of statistical characteristic indexes into support vector sequences. Then, the Gaussian function fitting (GFF) model based on density clustering is established to fit the aging curves of support vector sequence data under different kernel functions, and the Fréchet distance optimization algorithm is used to select the optimal aging curve. Finally, based on the optimal aging curve, the remaining useful life before the system reaches the aging threshold is evaluated to predict when software aging occurs. The experiment results on four aging data sets of an OpenStack cloud server show that, the proposed cloud server aging prediction method based on remaining useful life and SVs-GFF has higher accuracy and faster convergence speed compared with traditional prediction methods.
- Cloud server /
- software aging /
- support vector regression (SVR) /
- Gaussian function fitting (GFF) /
- remaining useful life (RUL)

HTML全文

圖 1 云服務(wù)器老化現象

Fig. 1 Software aging phenomenon in a cloud server

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圖 2 基于SVs-GFF的云服務(wù)器老化預測方法框圖

Fig. 2 Block diagram of cloud server aging prediction method based on SVs-GFF

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圖 3 支持向量的空間分布

Fig. 3 Spatial distribution of support vectors

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圖 4 實(shí)驗平臺

Fig. 4 Test bed

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圖 5 OpenStack云服務(wù)器原始數據

Fig. 5 Original data of OpenStack cloud server

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圖 6 原始數據的統計特征指標

Fig. 6 Statistical characteristic index of origin data

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圖 7 基于Fréchet距離選取最優(yōu)老化曲線(xiàn)

Fig. 7 Select the optimal aging curve via the Fréchet distance

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圖 8 老化曲線(xiàn)擬合對比

Fig. 8 Comparison of aging curve fitting

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圖 9 云服務(wù)器老化預測結果

Fig. 9 Cloud server aging prediction results

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圖 10 云服務(wù)器RUL預測結果

Fig. 10 Cloud server RUL prediction results

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圖 11 云服務(wù)器RUL預測絕對誤差

Fig. 11 Absolute error of cloud server RUL prediction results

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圖 12 云服務(wù)器老化預測結果

Fig. 12 Cloud server aging prediction results

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圖 13 云服務(wù)器RUL預測結果

Fig. 13 Cloud server RUL prediction results

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圖 14 云服務(wù)器RUL預測絕對誤差

Fig. 14 Absolute error of cloud server RUL prediction results

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表 1 老化曲線(xiàn)擬合RMSE對比 (%)

Table 1 Comparison of aging curve fitting RMSE (%)

擬合方法	響應時(shí)間集	頁(yè)面傳輸速度集
基于密度聚類(lèi)的GFF	21.598	47.129
SVR	57.334	114.239

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表 2 不同預測方法的參數設置

Table 2 Parameter setting of different prediction methods

預測方法	參數設置
SVs-GFF	高斯函數中$\alpha$、$\beta$、$\sigma$和$\gamma$的初始值: 0, 尋優(yōu)方法: 最小二乘法, SVR中正則化參數: 1.0, 距離閾值$\varepsilon$: 0.5
SVR	正則化參數: 1, 核函數: RBF
GFF	高斯函數中$\alpha$、$\beta$、$\sigma$和$\gamma$的初始值: 0, 尋優(yōu)方法: 最小二乘法
PF	基于PF模型更新指數模型參數, 老化特征值$\alpha$: 1.979, 指數模型參數$b$: 0.00271, $c$: ?0.1697, 白噪聲標準差$d$: ?0.06942
ANN	神經(jīng)元數: [輸入層: 30, 隱藏層1: 64, 隱藏層2: 64, 隱藏層3: 32, 輸出層: 1 ], 激活函數: ReLU, 迭代次數: 100
LSTM	神經(jīng)元數: [輸入層: 10, 隱藏層1: 32, 隱藏層2: 32, 隱藏層3: 16, 輸出層: 1 ], 激活函數: ReLU, 迭代次數: 100
Markov	基于Markov模型更新指數模型參數, 指數模型參數的初始值: 0

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表 3 預測性能比較

Table 3 Comparison of prediction performances

數據集名稱(chēng)	評價(jià)指標	SVs-GFF	GFF	SVR	PF	ANN	LSTM	Markov
響應時(shí)間數據集	${\rm{CRA}}$	0.904	0.769	0.891	0.841	0.737	0.901	0.858
響應時(shí)間數據集	$C_{PE}$	15.117	15.896	16.035	15.369	18.353	19.360	15.371
頁(yè)面傳輸速度數據集	${\rm{CRA}}$	0.879	0.698	0.861	0.801	0.721	0.792	0.813
頁(yè)面傳輸速度數據集	$C_{PE}$	16.487	16.945	16.987	17.897	19.547	20.487	17.881

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