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摘要: 针对云服务器中存在软件老化现象, 将造成系统性能衰退与可靠性下降问题, 借鉴剩余使用寿命(Remaining useful life, RUL)概念, 提出基于支持向量和高斯函数拟合(Support vectors and Gaussian function fitting, SVs-GFF)的老化预测方法. 首先, 提取云服务器老化数据的统计特征指标, 并采用支持向量回归(Support vector regression, SVR)对统计特征指标进行数据稀疏化处理, 得到支持向量(Support vectors, SVs)序列数据; 然后, 建立基于密度聚类的高斯函数拟合(Gaussian function fitting, GFF)模型, 对不同核函数下的支持向量序列数据进行老化曲线拟合, 并采用Fréchet距离优化算法选取最优老化曲线; 最后, 基于最优老化曲线, 评估系统到达老化阈值前的RUL, 以预测系统何时发生老化. 在OpenStack云服务器4个老化数据集上的实验结果表明, 基于RUL和SVs-GFF的云服务器老化预测方法与传统预测方法相比, 具有更高的预测精度和更快的收敛速度.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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图 2 基于SVs-GFF的云服务器老化预测方法框图
Fig. 2 Block diagram of cloud server aging prediction method based on SVs-GFF
图 7 基于Fréchet距离选取最优老化曲线
Fig. 7 Select the optimal aging curve via the Fréchet distance
表 1 老化曲线拟合RMSE对比 (%)
Table 1 Comparison of aging curve fitting RMSE (%)
拟合方法 响应时间集 页面传输速度集 基于密度聚类的GFF 21.598 47.129 SVR 57.334 114.239 
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表 2 不同预测方法的参数设置
Table 2 Parameter setting of different prediction methods
预测方法 参数设置 SVs-GFF 高斯函数中$\alpha$、$\beta$、$\sigma$和$\gamma$的初始值: 0, 寻优方法: 最小二乘法, SVR中正则化参数: 1.0, 距离阈值$\varepsilon$: 0.5 SVR 正则化参数: 1, 核函数: RBF GFF 高斯函数中$\alpha$、$\beta$、$\sigma$和$\gamma$的初始值: 0, 寻优方法: 最小二乘法 PF 基于PF模型更新指数模型参数, 老化特征值$\alpha$: 1.979, 指数模型参数$b$: 0.00271 , $c$: −0.1697 , 白噪声标准差$d$: −0.06942 ANN 神经元数: [输入层: 30, 隐藏层1: 64, 隐藏层2: 64, 隐藏层3: 32, 输出层: 1 ], 激活函数: ReLU, 迭代次数: 100 LSTM 神经元数: [输入层: 10, 隐藏层1: 32, 隐藏层2: 32, 隐藏层3: 16, 输出层: 1 ], 激活函数: ReLU, 迭代次数: 100 Markov 基于Markov模型更新指数模型参数, 指数模型参数的初始值: 0
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表 3 预测性能比较
Table 3 Comparison of prediction performances
数据集名称 评价指标 SVs-GFF GFF SVR PF ANN LSTM Markov 响应时间数据集 ${\rm{CRA}}$ 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 页面传输速度数据集 ${\rm{CRA}}$ 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
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