# Dynamic Task Scheduling Scheme for Processing Real-Time Stream Data in Storm Environments
## 4 Performance Evaluation
In addition, the quality of service (QoS) rate is examined for evaluating the performance. The QoS rate refers to the ration of the processed time to the deadline. For example, a QoS rate of 80% indicates that the task was completed within 80% of the deadline. This rate can be used to examine whether a task can be completed within a deadline. Figure 8 illustrates the QoS rates based on the query types. Figure 8a shows the QoS rate for a simple query. When the CPU usage is 20%, the tasks are processed within 90% of the deadline because the number of tasks is small. When the CPU usage is increased to 50-80% during real-time stream processing, the usage also increases but all the tasks are processed within the deadline. Figure 8b shows the QoS rate for a complex query. Similar to a simple query, the tasks are processed within 95% of the deadline if the usage is not high. Similarly, the proposed dynamic scheduling considering deadlines is valid because all tasks are processed within the deadline. The proposed scheme is compared with existing scheduling schemes to verify the excellence of the former. The processing times are compared based on real-time data of various sizes and combination of queries (simple query + complex query). The conventional scheme, R-Storm, and the dynamic scheduling scheme proposed by [PERSON], adaptive scheduler, are comparatively evaluated. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameter** & **Value** \\ \hline \multirow{4}{*}{Query} & Data size & 1 GB, 2 GB \\  & Data composition & English alphabet, random number \\  & Simple Query & Word count \\  & Complex Query & k-means algorithm \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Query type of performance evaluation. Figure 9 shows the performance evaluation based on the data size and the query. Word count and k-means clustering were performed using text files of 1 GB and 2 GB consisting of English alphabets and random numbers separately. R-Storm has the highest task processing time and increases the processing costs of the worker nodes owing to its computational complexity. The adaptive scheduler scheme and the proposed dynamic scheduling scheme process real-time data within 45 s for the 2 GB data. The proposed scheme reduces the processing time by 11-15% on average by performing a scheduling in which the task deadline and various loads are considered. Figure 7: Processing times based on resource usage. (**a**) Processing time for simple query; (**b**) processing time for complex query. In addition, the QoS rates of the tasks were examined. In Figure 10a, the average QoS rate of each worker node when processing a simple query is shown. The QoS rate is within 100% when the data size is small, thus indicating that all tasks are processed within the deadline. The proposed scheme has a relatively long delay time for a simple query. However, as shown in Figure 10b, the QoS rates of all conventional schemes exceed 100% for a complex query, thus having a delay in the tasks. This delay is caused by failing to consider the task deadline. The proposed scheme does not pass the deadline regardless of the data size because the deadline as well as CPU and memory usage of the worker nodes were considered, and exhibits a 9-11% improved performance compared to the existing schemes. Figure 8: QoS rates based on resource usage. (**a**) QoS rate for simple query; (**b**) QoS rate for complex query. Figure 10: QoS rates based on data sizes. (**a**) QoS rate for simple query; (**b**) QoS rate for complex query. Figure 9: Query processing time based on data size.