# Dynamic Task Scheduling Scheme for Processing Real-Time Stream Data in Storm Environments
## 4 Performance Evaluation  
For verifying the excellence of the proposed dynamic task scheduling scheme, its processing speed was measured under each load condition, and the performance was evaluated for task scheduling proposed using R-Storm [12] and J. Fan [13]. The two existing methods we have chosen for performance evaluation are the most appropriate for performance comparison with the proposed method as they perform scheduling with only collectable memory usage, CPU, and network traffic information. The performance evaluation parameters are listed in Table 3. The environment consists of one master and ten worker nodes, in which the data processing speeds for complex and simple queries as well as the task delay of each worker node are evaluated. The threshold for determining a load is set as 80%. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Parameter**} & \multicolumn{2}{c}{**Value**} \\ \hline \multicolumn{2}{c}{Operating System} & \multicolumn{2}{c}{CentOS 6.6} \\ \multicolumn{2}{c}{Master} & Processor & Intel(R) Core(TM) i5-3570K \\ \multicolumn{2}{c}{Memory} & 4 GB \\ \multicolumn{2}{c}{Processor} & Intel(R) Core(TM) i7-3610 QM \\ \multicolumn{2}{c}{Memory} & 4 GB \\ \multicolumn{2}{c}{Processor} & Intel(R) Core(TM) i5-3570K \\ \multicolumn{2}{c}{Memory} & 2 GB \\ \multicolumn{2}{c}{Processor} & Intel(R) Core(TM) i5-3570K \\ \multicolumn{2}{c}{Memory} & Intel(R) Core(TM) i5-3570K \\ \multicolumn{2}{c}{Index node 3–10} & \multicolumn{2}{c}{Memory} & 2 GB \\ \multicolumn{2}{c}{Disk} & \multicolumn{2}{c}{500 GB} \\ \multicolumn{2}{c}{Program Language} & \multicolumn{2}{c}{Java} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance evaluation parameters. Figure 6: CPU and memory load-based scheduling. Table 4 summarizes the types of data and queries used in the performance evaluation. The dataset used in the performance evaluation includes text files of 1 GB and 2 GB sizes consisting of English letters and of the same size consisting of random numbers. The performance evaluation data were transmitted in real time to construct a stream environment. Accordingly, the word count corresponding to a simple query and the k-means algorithm, which corresponds to a complex query, are processed [22]. The k-means algorithm is an algorithm for grouping data into \(k\) number of clusters, where the distribution of the distances from each cluster is minimized. The factors that significantly influence the computational complexity of the k-means algorithm are Euclidean space \(d\) and the number of clusters \(k\). Even if the number of clusters is small, finding the optimal solution of the k-means algorithm in general Euclidean space \(d\) is NP-hard [23; 24]. Finding the optimal solution of \(k\) number of clusters even in a low-dimensional Euclidean space is also NP-hard [25]. Therefore, the task complexity in the k-means algorithm is high because a heuristic technique is used. The processing speeds of the tasks and the real-time processing rates are comparatively analyzed by classifying the tasks into simple and complex tasks. When processing real-time stream data, the task processing speed and the delay time vary with the load. Therefore, they must be examined with respect to the query of the proposed scheme. First, the speed of processing the tasks based on the worker node load and the rate of processing the tasks within the deadline are examined; subsequently, the performance is evaluated by comparing to previously proposed schemes. Text files of size 1 GB consisting of English alphabets and random numbers, respectively, are used. Figure 7 illustrates the processing times of the proposed scheme for queries with respect to the CPU and memory usage. Figure 7a shows the processing time for a simple query, such as word count, and Figure 7b shows that for a complex query, such as k-means clustering. The processing times of the tasks varied based on the CPU usage, whereas no significant changes were observed with respect to the memory usage.