Online Reliability Prediction via Long Short Term Memory for Service-Oriented Systems

被引：27

作者：

Wang, Hongbing ^{[1
,2
]}

Yang, Zhengping ^{[1
,2
]}

Yu, Qi ^{[3
]}

机构：

[1] Southeast Univ, Sch Comp Sci & Engn, Nanjing 210096, Jiangsu, Peoples R China

[2] Southeast Univ, Key Lab Comp Network & Informat Integrat, Nanjing 210096, Jiangsu, Peoples R China

[3] Rochester Inst Technol, Coll Comp & Informat Sci, 102 Lomb Mem Dr, Rochester, NY 14623 USA

来源：

2017 IEEE 24TH INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS 2017) | 2017年

关键词：

Online Reliability Prediction; Time Series; Service-Oriented Computing; System of Systems;

D O I：

10.1109/ICWS.2017.19

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A service-oriented System of System (SoS) integrates component services into a value-added and more complex system to satisfy the complex requirements of users. Due to a dynamic running environment, online reliability prediction for the loosely coupled component systems that ensures the runtime quality poses a major challenge and attracts growing attention. To guarantee the stable and continuous operation of systems, we propose a online reliability time series prediction method basing on long short term memory (LSTM), which is a modified Recurrent Neural Networks trained with historical reliability time series to predict the reliability of component systems in the near future. We conduct a series of experiments on a dataset composed of real web services and compare with other competitive approaches. Experimental results have demonstrated the effectiveness of our approach.

引用

页码：81 / 88

页数：8

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