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

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.