\cite{TOP500_Supercomputers_Sites}. However, it was also the most power hungry
platform with its over 3 million cores consuming around 17.8 megawatts.
Moreover, according to the U.S. annual energy outlook 2015
-\cite{U.S_Annual.Energy.Outlook.2014}, the price of energy for 1 megawatt-hour
+\cite{U.S_Annual.Energy.Outlook.2015}, the price of energy for 1 megawatt-hour
was approximately equal to \$70. Therefore, the price of the energy consumed by
the Tianhe-2 platform is approximately more than \$10 million each year. The
computing platforms must be more energy efficient and offer the highest number
50.32 kilowatts.
}
+\textcolor{blue}{
Besides platform improvements, there are many software and hardware techniques
to lower the energy consumption of these platforms, such as scheduling, DVFS,
\dots{} DVFS is a widely used process to reduce the energy consumption of a
the energy consumption of message passing iterative applications running over
homogeneous and heterogeneous clusters respectively.
The results of the experiments show significant energy
-consumption reductions. In this paper, a new frequency selecting algorithm
-adapted for heterogeneous platform is presented. It selects the vector of
+consumption reductions. All the experimental results were conducted over
+Simgrid simulator \cite{SimGrid}, which offers easy tools to create a homogeneous and heterogeneous platforms. In this paper, a new frequencies selecting algorithm
+adapted for heterogeneous grid platform is presented and executed over real testbed,
+the grid'5000 platform \cite{grid5000}. It selects the vector of
frequencies, for a heterogeneous grid platform running a message passing iterative
application, that simultaneously tries to offer the maximum energy reduction and
minimum performance degradation ratio. The algorithm has a very small overhead,
-works online and does not need any training or profiling.
+works online and does not need any training or profiling.}
\textcolor{blue}{
This paper is organized as follows: Section~\ref{sec.relwork} presents some
characteristics of each processor (computation power, range of frequencies,
dynamic and static powers) and the task executed (computation/communication
ratio). The aim being to reduce the overall energy consumption and to avoid
-increasing significantly the execution time. In our previous
-work~\cite{Our_first_paper,pdsec2015}, we proposed a method that selects the optimal
-frequency scaling factor for a homogeneous and heterogeneous clusters executing a message passing
+increasing significantly the execution time.
+\textcolor{blue}{ In our previous
+works~\cite{Our_first_paper} and \cite{pdsec2015}, we proposed a methods that select the optimal
+frequency scaling factors for a homogeneous and a heterogeneous clusters respectively.
+Both of the two methods executing a message passing
iterative synchronous application while giving the best trade-off between the
energy consumption and the performance for such applications. In this work we
-are interested in heterogeneous grid as described above. Due to the
+are interested in heterogeneous grid as described above.}
+Due to the
heterogeneity of the processors, a vector of scaling factors should be selected
and it must give the best trade-off between energy consumption and performance.
