\section{Introduction}
\label{sec.intro}
-\textcolor{blue}{
+\textcolor{red}{did you verify that these informations are still accurate before changing the years to 2015?}
The need for more computing power is continually increasing. To partially
satisfy this need, most supercomputers constructors just put more computing
nodes in their platform. The resulting platforms may achieve higher floating
which became the top of the Green500 list in June 2015 \cite{Green500_List}.
This heterogeneous platform executes more than 7 GFLOPS per watt while consuming
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
\cite{Our_first_paper} and \cite{pdsec2015} , a frequencies selecting algorithm was proposed to reduce
the energy consumption of message passing iterative applications running over
homogeneous and heterogeneous clusters respectively.
-The results of the experiments show significant energy
+The results of the experiments showed significant energy
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.}
+Simgrid simulator \cite{SimGrid}, which offers easy tools to create a homogeneous and heterogeneous platforms and run message passing parallel applications over them. In this paper, a new frequencies selecting algorithm,
+adapted to grid platforms, is presented. It is applied to the NAS parallel benchmarks and evaluated over a real testbed,
+the grid'5000 platform \cite{grid5000}. It selects for a grid platform running a message passing iterative
+application the vector of
+frequencies that simultaneously tries to offer the maximum energy reduction and
+minimum performance degradation ratios. The algorithm has a very small overhead,
+works online and does not need any training or profiling.
+
-\textcolor{blue}{
This paper is organized as follows: Section~\ref{sec.relwork} presents some
related works from other authors. Section~\ref{sec.exe} describes how the
execution time of message passing programs can be predicted. It also presents
an energy model that predicts the energy consumption of an application running
-over a heterogeneous grid. Section~\ref{sec.compet} presents the
+over a grid platform. Section~\ref{sec.compet} presents the
energy-performance objective function that maximizes the reduction of energy
consumption while minimizing the degradation of the program's performance.
Section~\ref{sec.optim} details the proposed frequencies selecting algorithm.
Section~\ref{sec.expe} presents the results of applying the algorithm on the
-NAS parallel benchmarks and executing them on a grid'5000 testbed.
+NAS parallel benchmarks and executing them on the grid'5000 testbed.
It shows the results of running different scenarios using multi-cores and one core per node
-and comparing them. It also shows the results of running
-three different power scenarios and comparing them. Moreover, it shows the
+and comparing them. It also evaluates the algorithm over three different power scenarios. Moreover, it shows the
comparison results between the proposed method and an existing method. Finally,
-in Section~\ref{sec.concl} the paper ends with a summary and some future works.}
+in Section~\ref{sec.concl} the paper ends with a summary and some future works.
\section{Related works}
\label{sec.relwork}
