# Application of Workflow Technology for Big Data Analysis Service
## 4 Experiments
### Efficiency of ChainWF Mechanism  
The data analysis workflow shown in Figure 20 is taken as an example. We evaluated the performance of the proposed cloud-based workflow system by comparing three different execution forms: without workflow system and executing tasks manually (NoneWF), common workflow (CommonWF), and chain workflow (ChainWF).  
The example workflow is a Groupby operation which includes a normal business (\"Input\" component) and two cloud businesses (\"Column generation\" and \"Groupby\" component). For ChainWF, the \"Generated Column\" and \"Groupby\" components are combined as a chain component in which analyzed data would not be written into hard drive and stored in the memory until they are completed. The analysis results would be stored into the specified location of HDFS or HBase according to users' configuration. The test data were actual Wireless Application Protocol (WAP) log data including 43 files (phone numbers, time,  
\begin{table}
\begin{tabular}{c c} \hline
**Web Attacks** & **Features** \\ \hline URL Guess & eWebEditor, Fockeditor, editor.Admin, Manage, System, login \\ SQL Injection Attacks & \%20 AnD\%201-1 \%20 AnD\%201=2 \\ SQL Injection Worm & DeClArE\%200, eXeC(@, vArChAr, /**/**/@ \\ Webshell Logs & Action=filesystem, fsAction=, @eval, mkdir, systeminfo, netstat, caidao \\ Struts2/Xwork Remote Execution & allowStaticMethodAccess, xwork, u0023, execute, PUT, wget, ps\%20- \\ \hline \end{tabular}
\end{table}
Table 1: Web attacks behavior features.  
\begin{table}
\begin{tabular}{c c c} \hline
**Node Name** & **IP** & **Roles** \\ \hline Node1 & 192.168.32.112 & Namenode, ResourceManager, Nimbus, ZooKeeper \\ Node2 & 192.168.32.113 & Datanode, supervisor \\ Node3 & 192.168.32.114 & Datanode, supervisor \\ Node4 & 192.168.32.115 & Datanode, supervisor \\ Node5 & 192.168.32.116 & Datanode, supervisor \\ Node6 & 192.168.32.117 & Datanode, supervisor \\ Node7 & 192.168.32.117 & Datanode, supervisor \\ Node8 & 192.168.32.119 & Datanode, supervisor \\ Node9 & 192.168.32.120 & Datanode, supervisor \\ Node10 & 192.168.32.121 & Datanode, supervisor \\ Node11 & 192.168.32.122 & Datanode, supervisor \\ Node12 & 192.168.32.123 & Datanode, supervisor \\ \hline \end{tabular}
\end{table}
Table 2: Cluster configuration information.  
Figure 20: Data analysis workflow.  
and WAP websites). The test results are shown in Figure 21. The horizontal axis presents the test data scale ranging from 50 GB to 230 GB, and the vertical axis is the run time.  
The experimental results show that the required run times of NoneWF and CommonWF were almost the same and higher than that of ChainWF. The results illustrate that integrating a workflow system into cloud-based data analysis systems could not impact the performance of the data analysis system. The workflow system makes it easy for users to use data analysis algorithms. The experiments show that the ChainWF improved the performance greatly compared with the CommonWF. ChainWF combines several components into a combination component which can avoid writing the output data of each component into the hard disk and utilizes the efficient computing capability of the cloud platform.