| 基于TransMP模型的Web系统剩余寿命预测方法 |
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| 引用本文： | 党伟超,张桄菖. 基于TransMP模型的Web系统剩余寿命预测方法[J]. 计算机应用研究, 2024, 41(6) |
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| 作者姓名： | 党伟超 张桄菖 |
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| 作者单位： | 太原科技大学, 计算机科学与技术学院,太原科技大学, 计算机科学与技术学院 |
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| 基金项目： | 太原科技大学博士科研启动基金资助项目(20202063);太原科技大学研究生联合培养示范基地项目(JD2022010) |
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| 摘 要： | 针对当前软件剩余使用寿命预测方法忽略了多性能指标间所蕴涵寿命信息的问题，提出一种融合多性能指标Transformer（TransMP）模型的Web系统剩余寿命预测方法。首先，搭建内存故障型Web系统加速老化实验平台，创建包含内存使用量、响应时间和吞吐率性能指标的数据集；其次，考虑不同性能指标蕴涵老化特征信息的差异性，构造由多编码器-解码器组成的TransMP模型，将性能指标数据分别输入内存指标编码器、响应时间编码器和吞吐率编码器提取老化特征信息，再引入特征融合层进行信息融合；最后，将融合信息输入由掩码注意力-多头注意力结构构成的解码器，预测得到系统状态达到老化阈值的剩余寿命。实验结果表明，该Web系统剩余寿命预测方法与最优的SALSTM方法相比，均方根误差分别降低了12.0%、17.3%和13.2%，平均绝对误差分别降低了13.3%、21.0%和10.4%，证明了该方法的有效性。
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| 关 键 词： | Web系统 软件老化 剩余使用寿命 Transfomer 软件再生 |
| 收稿时间： | 2023-10-11 |
| 修稿时间： | 2024-05-09 |
Remaining useful life prediction method of Web system based on TransMP model |
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| DangWeiChao and ZhangGuangChang. Remaining useful life prediction method of Web system based on TransMP model[J]. Application Research of Computers, 2024, 41(6) |
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| Authors: | DangWeiChao and ZhangGuangChang |
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| Affiliation: | College of Computer Science & Technology, Taiyuan University of Science & Technology, |
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| Abstract: | Focused on the problem of current software remaining life prediction methods ignores the life information contained among multiple performance indicators, this paper proposed a remaining life prediction method of Web software system based on the Transformer model with multiple performance indicators(TransMP). Firstly, it constructed an accelerated aging experimental platform for a memory fault type Web system, and created a dataset containing performance indicators such as memory usage, response time, and throughput. Secondly, considering the differences in aging characteristic information contained in different performance indicators, it constructed the TransMP model consisting of multiple encoders-decoders. The performance indicator data was separately input into the memory indicator encoder, response time encoder, and throughput encoder to extract aging characteristic information, and then introducing a feature fusion layer for information fusion. Finally, it input the fused information into the decoder composed of a mask attention-multi-head attention structure to predict the remaining life when the system reached an aging threshold. The experimental results indicate that the remaining life prediction method of the Web system, compared to the SALSTM method, reduces the root mean square error by 12.0%, 17.3% and 13.2%, and decreases the mean absolute error by 13.3%, 21.0% and 10.4%, demonstrating the effectiveness of this method. |
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| Keywords: | Web system software aging remaining useful life(RUL) Transformer software rejuvenation |
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