中文

Journal of Applied Sciences >

2023 , Vol. 41 >Issue 1: 121 - 140

DOI: https://doi.org/10.3969/j.issn.0255-8297.2023.01.010

Special Issue on Computer Applications

Proactive Self-Adaptive Approach Driven by LSTM Prediction for Software System

Expand
  • 1. College of Mathematics and Computer Science, Yan'an University, Yan'an 716000, Shaanxi, China;
    2. School of Computer Science and Technology, Xidian University, Xi'an 710071, Shaanxi, China

Received date: 2022-06-23

  Online published: 2023-02-03

Fold

Abstract

Aiming at the adjustment lag problem of reactive self-adaptive software systems, a proactive self-adaptive approach based on long short-term memory (LSTM) prediction driven is proposed. In this approach, LSTM neural network prediction technology is embedded in the analysis phase of monitor-analyze-plan -execute-knowledge (MAPE-K) control model; Operating data relating to self-adaptive environments, self-adaptive qualities, and self-adaptive goals, and historical data are used for classification prediction to form a self-adaptive early warning mechanism, which can effectively improve the proactive selfadaptive ability of software systems and reduce the lag influence of reactive self-adaptive decision-making at the same time. In order to illustrate the initiative, robustness and effectiveness of this approach, evaluation on the classic distribution tele-assistance system (dTAS) platform is carried out. Experimental results show that the proposed approach can provide early warning to self-adaptive demand, and enable software systems to complete proactive self-adaptive adjustment when necessary.

Key words： software self-adaptation; proactive self-adaptation; prediction driven; selfadaptive early warning

Cite this article

XIE Shenglong, WANG Lu, LIU Ruijia, PU Ying, LIU Xiao . Proactive Self-Adaptive Approach Driven by LSTM Prediction for Software System[J]. Journal of Applied Sciences, 2023 , 41(1) : 121 -140 . DOI: 10.3969/j.issn.0255-8297.2023.01.010

References

[1] Aase K K. Elements of economics of uncertainty and time with recursive utility[J]. Discussion Papers, 2020. DOI:10.2139/SSRN.3725505.
[2] Huang G L, Zhang M, Montiel D, et al. Automated extraction of physical parameters from experimentally obtained thermal profiles using a machine learning approach[J]. Computational Materials Science, 2021, 194:110459. DOI:10.1016/j.commatsci.2021.110459.
[3] Padilla L M K, Powell M, Kay M, et al. Uncertain about uncertainty:how qualitative expressions of forecaster confidence impact decision-making with uncertainty visualizations[J]. Frontiers in Psychology, 2021, 11:579267.
[4] Moreno G A, Cámara J, Garlan D, et al. Proactive self-adaptation under uncertainty:a probabilistic model checking approach[C]//Joint Meeting on Foundations of Software Engineering, ACM, 2015:1-12.
[5] Ren J H, Liu F. Predicting software defects using self-organizing data mining[J]. IEEE Access, 2019, 99:1. DOI:10.1109/ACCESS.2019.2927489.
[6] Cámar A J, Moreno G A, Garlan D. Stochastic game analysis and latency awareness for self-adaptation[C]//International Symposium on Software Engineering for Adaptive & Selfmanaging Systems, 2014:155-164.
[7] Ehrig H, Ermel C, Golas U, et al. Modelling and static analysis of self-adaptive systems by graph transformation[M]. Berlin, Heidelberg:Springer, 2015:299-326.
[8] Bucchiarone A, Ehrig H, Ermel C, et al. Rule-based modeling and static analysis of selfadaptive systems by graph transformation[M]. Cham:Springer, 2015:582-601.
[9] Fuad M M, Deb D, Baek J. Static analysis, code transformation and runtime profiling for self-healing[J]. Journal of Computers, 2013, 8(5):1127-1135.
[10] Bodden E. Self-adaptive static analysis[C]//International Conference on Software Engineering:New Ideas and Emerging Results, 2018:45-48.
[11] Sayre D B. A runtime verification and validation framework for self-adaptive software[D]. Fort Lauderdale:Nova Southeastern University, 2017.
[12] Calinescu R, Ghezzi C, Kwiatkowska M, et al. Self-adaptive software needs quantitative verification at runtime[J]. Communications of the ACM, 2012, 55(9):69-72, 75-77.
[13] Gerasimou S, Calinescu R, Banks A. Efficient runtime quantitative verification using caching, lookahead, and nearly-optimal reconfiguration[M].[S.l.]:ACM, 2014.
[14] Cámara J, Muccini H, Vaidhyanathan K. Quantitative verification-aided machine learning:a tandem approach for architecting self-adaptive IoT systems[C]//2020 International Conference on Software Architecture, 2020:11-22.
[15] 熊伟, 李兵, 陈军, 等. 一种基于预测控制的SaaS系统自适应方法[J]. 计算机学报, 2016, 39(2):364-376. Xiong W, Li B, Chen J, et al. A self-adaptation approach based on predictive control for SaaS[J]. Chinese Journal of Computers, 2016, 39(2):364-376. (in Chinese)
[16] Esfahani N, Elkhodary A, Malek S. A learning-based framework for engineering featureoriented self-adaptive software systems[J]. IEEE Transactions on Software Engineering, 2013, 39(11):1467-1493.
[17] Hw A, Lei W, Qi Y C, et al. A proactive approach based on online reliability prediction for adaptation of service-oriented systems[J]. Journal of Parallel and Distributed Computing, 2018, 114:70-84.
[18] 王璐, 李青山, 吕文琪, 等. 基于事件关系保障识别质量的自适应分析方法[J]. 软件学报, 2021, 32(7):1978-1998. Wang L, Li Q S, Lü W Q, et al. Self-adaptation analysis method for recognition quality assurance using event relationships[J]. Journal of Software, 2021, 32(7):1978-1998. (in Chinese)
[19] Aschoff R R, Zisman A. Proactive adaptation of service composition[J]. Lecture Notes in Computer Science, 2012, 7084(11):1-10. DOI:10.1109/SEAMS.2012.6224385.
[20] Zhang X, Li B, Zhu J. A monitoring and prediction model of workflow based self-adaptive software system[C]//The Second International Conference on Advanced Cloud & Big Data, IEEE Computer Society, 2014:115-121.
[21] Babu G S, Zhao P, Li X L. Deep convolutional neural network based regression approach for estimation of remaining useful life[C]//International Conference on Database Systems for Advanced Applications. Cham:Springer, 2016:214-228.
[22] Li X, Ding Q, Sun J Q. Remaining useful life estimation in prognostics using deep convolution neural networks[J]. Reliability Engineering & System Safety, 2018, 172:1-11.
[23] Mei Y, Wu Y, Li L. Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network[C]//IEEE International Conference on Aircraft Utility Systems, 2016:135-140.
[24] Zhang Y. Aeroengine fault prediction based on bidirectional LSTM neural network[C]//2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, 2020:317-320.
[25] Nguyen K, Medjaher K. A new dynamic predictive maintenance framework using deep learning for failure prognostics[J]. Reliability Engineering System Safety, 2019, 188:251-262.
[26] Huang C G, Yin X, Huang H Z, et al. An enhanced deep learning-based fusion prognostic method for RUL prediction[J]. IEEE Transactions on Reliability, 2019, 99:1-13.
[27] 盛剑会, 肖冬荣, 郭伟, 等. 模型预测控制(MPC)系统仿真软件开发与实现[J]. 辽宁工程技术大学学报(自然科学版), 2004, 23(2):226-229. Sheng J H, Xiao D R, Guo W, et al. Development and implementation of simulating software of model predictive control (MPC) system[J]. Journal of Liaoning Technical University (Natural Science Edition), 2004, 23(2):226-229. (in Chinese)
[28] 赵天琪, 赵海燕, 张伟, 等. 基于模型的自适应方法综述[J]. 软件学报, 2018, 29(1):23-41 Zhao T Q, Zhao H Y, Zhang W, et al. Survey of model-based self-adaptation methods[J]. Journal of Software, 2018, 29(1):23-41. (in Chinese)
[29] 于少伟. 基于区间数的模糊隶属函数构建[J]. 山东大学学报(工学版), 2010, 40(6):32-35, 93. Yu S W. Construction of a fuzzy membership function based on interval number[J]. Journal of Shandong University (Engineering Science), 2010, 40(6):32-35, 93. (in Chinese)
[30] Gordieiev O, Kharchenko V, Fominykh N, et al. Evolution of software quality models in context of the standard ISO 25010[C]//Proceedings of the Ninth International Conference on Dependability and Complex Systems DepCoS-RELCOMEX, 2014:223-232.
[31] Chen T, Bahsoon R. Self-adaptive and online QoS modeling for cloud-based software services[J]. IEEE Transactions on Software Engineering, 2017:1.
[32] Magableh B. A framework for evaluating model-driven self-adaptive software systems[J]. International Journal of Information Technology and Computer Science, 2019:1-11.
[33] Weyns D. An introduction to self-daptive systems:a contemporary software engineering perspective[M]. UK:CPI Group, 2020.
[34] 蔺瑞管, 王华伟, 车畅畅, 等. 基于LSTM分类器的航空发动机预测性维护模型[J]. 系统工程与电子技术, 2022, 44(3):1052-1059. Lin R G, Wang H W, Che C C, et al. Predictive maintenance model of aeroengine based on LSTM classifier[J]. Systems Engineering and Electronics, 2022, 44(3):1052-1059. (in Chinese)
[35] Weyns D, Calinescu R. Tele assistance:a self-adaptive service-based system exemplar[C]//International Symposium on Software Engineering for Adaptive & Self-managing Systems, 2015:88-92.
Options
Outlines

/

〈 〉