\subsection{The results for different power consumption scenarios}
-
+\label{sec.compare}
The results of the previous section were obtained while using processors that consume during computation
an overall power which is 80\% composed of dynamic power and 20\% of static power. In this section,
these ratios are changed and two new power scenarios are considered in order to evaluate how the proposed
table~(\ref{table:platform}), it takes on average \np[ms]{0.04} for 4 nodes and \np[ms]{0.15} on average for 144 nodes
to compute the best scaling factors vector. The algorithm complexity is $O(F\cdot (N \cdot4) )$, where $F$ is the number
of iterations and $N$ is the number of computing nodes. The algorithm needs from 12 to 20 iterations to select the best
-vector of frequency scaling factors that gives the results of the section (\ref{sec.res}).
+vector of frequency scaling factors that gives the results of the sections (\ref{sec.res}) and (\ref{sec.compare}) .
\section{Conclusion}
\label{sec.concl}
-
+In this paper, we have presented a new online heterogeneous scaling algorithm
+that selects the best possible vector of frequency scaling factors. This vector
+gives the maximum distance (optimal tradeoff) between the normalized energy and
+the performance curves. In addition, we developed 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
+known in advance and depends on the global convergence of the iterative system.
\section*{Acknowledgment}
+
% trigger a \newpage just before the given reference
% number - used to balance the columns on the last page
% adjust value as needed - may need to be readjusted if
series = {IPDPS '05},
year = {2005},
isbn = {0-7695-2312-9},
- pages = {4.1--},
+ pages = {4a-4a},
doi = {10.1109/IPDPS.2005.214},
acmid = {1054466},
publisher = {IEEE Computer Society},
institution = {{DTIC} Document},
author = {Luley, Ryan and Usmail, Courtney and Barnell, Mark},
year = {2011},
- file = {a548738.pdf:files/30/a548738.pdf:application/pdf}
+
}
year = "2013",
issn = "0167-739X",
doi = "http://dx.doi.org/10.1016/j.future.2013.02.010",
-url = "http://www.sciencedirect.com/science/article/pii/S0167739X13000484",
author = {Lizhe Wang and Samee U. Khan and Dan Chen and Joanna Kołodziej and Rajiv Ranjan and Cheng-zhong Xu and Albert Zomaya}
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volume = {37},
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number = {4},
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journal = {{ACM} {SIGMETRICS} Performance Evaluation Review},
@article{Chen_DVFS.under.quality.of.service.requirements,
title = {Dynamic frequency scaling schemes for heterogeneous clusters under quality of service requirements},
volume = {28},
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number = {6},
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journal = {Journal of Information Science and Engineering},