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FD4C: Automatic Fault Diagnosis Framework for Web Applications in Cloud Computing
文献类型：期刊论文
| 作者 | Wang, T ; Zhang, WB ; Ye, CY ; Wei, J ; Zhong, H ; Huang, T |
| 刊名 | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
 |
| 出版日期 | 2016 |
| 卷号 | 46期号:1页码:61-75 |
| 关键词 | Cloud computing fault diagnosis performance anomaly software monitoring Web applications |
| ISSN号 | 2168-2216 |
| 中文摘要 | 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. |
| 英文摘要 | 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. |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000367142100006 |
| 公开日期 | 2016-12-13 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/17418]  |
| 专题 | 软件研究所_软件所图书馆_期刊论文 |
| 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. |
入库方式： OAI收割
来源：软件研究所
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