基于RUL和SVs-GFF的云服務(wù)器老化預測方法
doi: 10.16383/j.aas.c211112
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西安理工大學(xué)計算機科學(xué)與工程學(xué)院 西安 710048
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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
Cloud Server Aging Prediction Method Based on RUL and SVs-GFF
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School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048
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Shaanxi Key Laboratory Network Computer and Security Technology, Xi'an 710048
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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.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.
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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
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 下載: 導出CSV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 下載: 導出CSV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 $C_{PE}$ 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 $C_{PE}$ 16.487 16.945 16.987 17.897 19.547 20.487 17.881 下載: 導出CSV亚洲第一网址_国产国产人精品视频69_久久久久精品视频_国产精品第九页 -
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