FD4C: Automatic Fault Diagnosis Framework for Web Applications in Cloud Computing

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	FD4C: Automatic Fault Diagnosis Framework for Web Applications in Cloud Computing
	Wang, T; Zhang, WB; Ye, CY; Wei, J; Zhong, H; Huang, T
	2016
发表期刊	IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
ISSN	2168-2216
卷号	46 期号:1 页码:61-75
摘要	The large-scale dynamic cloud computing environment has raised great challenges for fault diagnosis in Web applications: First, fluctuating workloads cause traditional application models to change over time; second, modeling the behaviors of complex applications usually requires domain knowledge which is difficult to obtain; third, managing large-scale applications manually is impractical for operators. To address these issues, this paper proposes an automatic fault (F) diagnosis (D) framework for (4) Web applications in cloud (C) computing (FD4C). In this paper, we propose an online incremental clustering method to recognize access behavior patterns. We also use correlation analysis to model the correlations between the workloads and application performance/resource utilization metrics in a specific access behavior pattern. FD4C detects faults by discovering the abrupt changes of correlation coefficients with control charts. Then, FD4C identifies the fault-related metrics using a feature selection method. To evaluate our proposal, we inject typical faults into TPC-W benchmark and apply FD4C to diagnose the injected faults. The experimental results show that FD4C can effectively detect the typical faults and accurately locate the metrics related to the faults.; The large-scale dynamic cloud computing environment has raised great challenges for fault diagnosis in Web applications: First, fluctuating workloads cause traditional application models to change over time; second, modeling the behaviors of complex applications usually requires domain knowledge which is difficult to obtain; third, managing large-scale applications manually is impractical for operators. To address these issues, this paper proposes an automatic fault (F) diagnosis (D) framework for (4) Web applications in cloud (C) computing (FD4C). In this paper, we propose an online incremental clustering method to recognize access behavior patterns. We also use correlation analysis to model the correlations between the workloads and application performance/resource utilization metrics in a specific access behavior pattern. FD4C detects faults by discovering the abrupt changes of correlation coefficients with control charts. Then, FD4C identifies the fault-related metrics using a feature selection method. To evaluate our proposal, we inject typical faults into TPC-W benchmark and apply FD4C to diagnose the injected faults. The experimental results show that FD4C can effectively detect the typical faults and accurately locate the metrics related to the faults.
收录类别	SCI
关键词	Cloud Computing Fault Diagnosis Performance Anomaly Software Monitoring Web Applications
部门归属	Chinese Acad Sci, Inst Software, Beijing 100190, Peoples R China. Hainan Univ, Coll Informat Sci & Technol, Hainan 570228, Peoples R China. Chinese Acad Sci, Inst Software, State Key Lab Comp Sci, Beijing 100190, Peoples R China.
语种	英语
WOS记录号	WOS:000367142100006
引用统计
内容类型	期刊论文
URI标识	http://ir.iscas.ac.cn/handle/311060/17418
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Wang, T,Zhang, WB,Ye, CY,et al. FD4C: Automatic Fault Diagnosis Framework for Web Applications in Cloud Computing[J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,2016,46(1):61-75.
APA	Wang, T,Zhang, WB,Ye, CY,Wei, J,Zhong, H,&Huang, T.(2016).FD4C: Automatic Fault Diagnosis Framework for Web Applications in Cloud Computing.IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,46(1),61-75.
MLA	Wang, T,et al."FD4C: Automatic Fault Diagnosis Framework for Web Applications in Cloud Computing".IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 46.1(2016):61-75.

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