\label{fig:nas}
\end{figure*}
\begin{table}[htb]
- \caption{Optimal Scaling Factors Results}
+ \caption{The EPSA Scaling Factors Results}
% title of Table
\centering
- \begin{tabular}{ | l | l | l |l | r |}
- \AG{Use the same number of decimals for all numbers in a column,
- and vertically align the numbers along the decimal points.
- The same for all the following tables.}
+ \begin{tabular}{|l|*{4}{r|}}
\hline
Program & Optimal & Energy & Performance&Energy-Perf.\\
Name & Scaling Factor& Saving \%&Degradation \% &Distance \\ \hline
\includegraphics[width=.33\textwidth]{compare_class_A.pdf}
\includegraphics[width=.33\textwidth]{compare_class_B.pdf}
\includegraphics[width=.33\textwidth]{compare_class_c.pdf}
- \caption{Comparing Our EPSA with Rauber and R\"{u}nger Methods}
+ \caption{Comparing Our EPSA with Rauber and Rünger Methods}
\label{fig:compare}
\end{figure}
-
\section{Conclusion}
\label{sec.concl}
-
-\AG{the conclusion needs to be written\dots{} one day}
+In this paper we develop the simultaneous energy-performance algorithm. It is works based on the trade off relation between the energy and performance. The results showed that when the scaling factor is big value leads to more energy saving. Also, it show that when the the scaling factor is small value leads to the fact that the scaling factor has bigger impact on performance than energy. Then the algorithm optimize the energy saving and performance in the same time to have positive trade off. The optimal trade off refer to maximum distance between the energy and the inversed performance curves. Also, the results explained when setting the slowest task to maximum frequency usually not have a big improvement on performance.
\section*{Acknowledgment}
