\EndWhile
\State Return $Sopt_1,Sopt_2,\dots,Sopt_N$
\end{algorithmic}
- \caption{Heterogeneous scaling algorithm}
+ \caption{frequency scaling factors selection algorithm}
\label{HSA}
\end{algorithm}
\section{Conclusion}
\label{sec.concl}
-In this paper, a new online frequency selecting algorithm have been presented. It selects the best possible vector of frequency scaling factors for a heterogeneous platform.
-This vector gives the maximum distance (optimal tradeoff) between the predicted energy and
-the predicted performance curves. In addition, we developed a new energy model for measuring
+In this paper, a new online frequency selecting algorithm have been presented. It selects the best possible vector of frequency scaling factors that gives the maximum distance (optimal tradeoff) between the predicted energy and
+the predicted performance curves for a heterogeneous platform. This algorithm uses a new energy model for measuring
and predicting the energy of distributed iterative applications running over heterogeneous
-cluster. The proposed method evaluated on Simgrid/SMPI simulator to built a heterogeneous
-platform to executes NAS parallel benchmarks. The results of the experiments showed the ability of
-the proposed algorithm to changes its behaviour to selects different scaling factors when
-the number of computing nodes and both of the static and the dynamic powers are changed.
-
-In the future, we plan to improve this method to apply on asynchronous iterative applications
-where each task does not wait the others tasks to finish there works. This leads us to develop a new
-energy model to an asynchronous iterative applications, where the number of iterations is not
+platform. To evaluate the proposed method, it was applied on the NAS parallel benchmarks and executed over a heterogeneous platform simulated by Simgrid. The results of the experiments showed that the algorithm reduces up to 35\% the energy consumption of a message passing iterative method while limiting the degradation of the performance. The algorithm also selects different scaling factors according to the percentage of the computing and communication times, and according to the values of the static and dynamic powers of the CPUs.
+
+In the near future, this method will be applied to real heterogeneous platforms to evaluate its performance in a real study case. It would also be interesting to evaluate its scalability over large scale heterogeneous platform and measure the energy consumption reduction it can produce. Afterward, We would like to develop a similar method that is adapted to asynchronous iterative applications
+where each task does not wait for others tasks to finish there works. The development of such method might require a new
+energy model because the number of iterations is not
known in advance and depends on the global convergence of the iterative system.
\section*{Acknowledgment}
