\maketitle
+
+\begin{abstract}
+
+ In recent years, green computing topic has being became an important topic in
+ the domain of the research. The increase in computing power of the computing
+ platforms is increased the energy consumption and the carbon dioxide emissions.
+ Many techniques have being used to minimize the cost of the energy consumption
+ and reduce environmental pollution. Dynamic voltage and frequency scaling (DVFS)
+ is one of these techniques. It used to reduce the power consumption of the CPU
+ while computing by lowering its frequency. Moreover, lowering the frequency of
+ a CPU may increase the execution time of an application running on that
+ processor. Therefore, the frequency that gives the best trade-off between
+ the energy consumption and the performance of an application must be selected.
+
+ In this paper, a new online frequency selecting algorithm for heterogeneous
+ grid (heterogeneous CPUs) is presented. It selects the frequencies and tries to give the best
+ trade-off between energy saving and performance degradation, for each node
+ computing the message passing iterative application. The algorithm has a small
+ overhead and works without training or profiling. It uses a new energy model
+ for message passing iterative applications running on a heterogeneous
+ grid. The proposed algorithm is evaluated on real testbed, grid'5000 platform, while
+ running the NAS parallel benchmarks. The experiments show that it reduces the
+ energy consumption on average up to \np[\%]{30} while declines the performance
+ on average by \np[\%]{3} only for the same instance. Finally, the algorithm is
+ compared to an existing method, the comparison results show that it outperforms the
+ latter in term of energy and performance trade-off.
+\end{abstract}
+
+
\section{Introduction}
\label{sec.intro}
\textcolor{blue}{
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
+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.}
