[1] |
GROTTKE M , MATIAS R , TRIVEDI K S . The fundamentals of software aging[C]// 2008 IEEE International Conference on Software Reliability Engineering Workshops. Piscataway:IEEE, 2008: 1-6.
|
[2] |
GROTTKE M , TRIVEDI K S . Fighting bugs:remove,retry,replicate,and rejuvenate[J]. Computer, 2007,40: 107-109.
|
[3] |
HUANG Y , KINTALA C , KOLETTIS N ,et al. Software rejuvenation:analysis,module and applications[C]// 1995 Twenty-Fifth International Symposium on Fault-Tolerant Computing. Piscataway:IEEE Press, 1995: 381-390.
|
[4] |
OKAMURA H , LUO C , DOHI T . Estimating response time distribution of server application in software aging phenomenon[C]// 2013 International Symposium On Software Reliability Engineering Workshops. Piscataway:IEEE Press, 2013: 281-284.
|
[5] |
MATOS R , ARAUJO J , MACIEL P ,et al. A hybrid method based on multiple thresholds and time series prediction.international transaction on systems science and applications[J]. Software Rejuvenation in Eucalyptus Cloud Computing Infrastructure, 2012,8: 1-16.
|
[6] |
ISLAM J , KEUNG J , LEE K , LIU A . Empirical prediction models for adaptive resource provisioning in the cloud[J]. Future Generation Computer Systems, 2012,28(1): 155-162.
|
[7] |
LANGNER F , ANDRZEJAK A . Detecting software aging in a cloud computing framework by comparing development versions[C]// 2013 International Symposium on Integrated Network Management. Piscataway:IEEE Press, 2013: 896-899.
|
[8] |
KOUSIOURIS G , CUCINOTTA T , VARVARIGOU T . The effects of scheduling,workload type and consolidation scenarios on virtual machine performance and their prediction through optimized artificial neural networks[J]. Journal of Systems and Software, 2011,84(8): 1270-1291.
|
[9] |
郑鹏飞, 齐勇, 陈鹏飞 . 软件老化的多元时间序列分析方法[J]. 计算机科学与探索, 2012(2): 33-41.
|
|
ZHENG P F , QI Y , CHEN P F . Multivariate time series analysis of software aging[J]. Journal of Frontiers of Computer Science & Technology, 2012(2): 33-41.
|
[10] |
林已杰, 赖清, 周敏 . 基于 BP 神经网络和马尔可夫模型的服务器软件老化预测方法[J]. 西南师范大学学报(自然科学版), 2011,36(4): 193-197.
|
|
LIN Y J , LAI Q , ZHOU M . A study on software aging forecasting of web server in BP neural network methods & Markov model methods[J]. Journal of Southwest China Normal University(Natural Science Edition), 2011,36(4): 193-197.
|
[11] |
ABU A I A S , MAGHARI A Y A . Forecasting groundwater production and rain amounts using ARIMA-hybrid ARIMA:case study of deir El-Balah City in GAZA[C]// 2018 International Conference on Promising Electronic Technologies. Piscataway:IEEE Press, 2018: 135-140.
|
[12] |
YANG H , PAN Z , TAO Q . Online learning for vector autoregressive moving-average time series prediction[J]. Neurocomputing, 2018,315: 9-17.
|
[13] |
TRIANTAFYLLOPOULOS K , SHAKANDLI M , CAMPBELL M . Count time series prediction using particle filters[J]. Quality and Reliability Engineering International, 2019,35(4): 1445-1449.
|
[14] |
SAOUD L S , GHORBANI R . Metacognitive octonion-valued neural networks as they relate to time series analysis[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020,32(2): 539-548.
|
[15] |
ESKANDARPOUR R , KHODAEI A . Leveraging accuracy-uncertainty tradeoff in SVM to achieve highly accurate outage predictions[J]. IEEE Transactions on Power Systems, 2017,33(1): 1139-1141.
|
[16] |
LAMOURINE M . OpenStack[J]. Login::the Magazine of USENIX &SAGE, 2014,39: 17-20.
|
[17] |
ALBAROODI H , MANICKAM S , SINGH P . Critical review of OpenStack security:issues and weaknesses[J]. Journal of Computer Science, 2014,10(1): 1032.
|
[18] |
HORNIK K . Approximation capabilities of multilayer feedforward networks[J]. Neural Networks, 1991,4(2): 251-257.
|
[19] |
刘思峰, 蔡华, 杨英杰 ,等. 灰色关联分析模型研究进展[J]. 系统工程理论与实践, 2013(8): 2041-2046.
|
|
LIU S F , CAI H , YANG Y J ,et al. Advance in grey incidence analysis modelling[J]. Systems Engineering-Theory & Practice, 2013(8): 2041-2046.
|
[20] |
TORQUATO M , MACIEL P , ARAUJO J ,et al. An approach to investigate aging symptoms and rejuvenation effectiveness on software systems[C]// 2017 12th Iberian Conference on Information SystemsandTechnologies. Piscataway:IEEE Press, 2017: 1-6.
|
[21] |
WIDIYANINGTYAS T , MULADI , QONITA A . Use of ARIMA method to predict the number of train passenger in Malang City[C]// 2019 International Conference of Artificial Intelligence and Information Technology. Piscataway:IEEE Press, 2019: 359-364
|
[22] |
TOKG?Z , üNAL G , . A RNN based time series approach for forecasting turkish electricity load[C]// 2018 26th Signal Processing and Communications Applications Conference. Piscataway:IEEE Press, 2018: 1-4.
|