\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.
which on average is equal to 7\%.
\textcolor{blue}{
-The performance degradation percentages over one site multi-cores is lower because the computations to communications ratio is decreased. Therefore, selecting small
+The performance degradation percentages over one site multi-cores is lower because the computations to communications ratio is decreased. Therefore, selecting bigger
frequencies by the scaling algorithm are proportional to this ratio, and thus the execution time do not increase significantly.}
