\maketitle
\begin{abstract}
-Green computing emphasizes the importance of energy conservation, minimizing the negative impact
-on the environment while achieving high performance and minimizing operating costs. So, energy reduction
-process in a high performance clusters it can be archived using dynamic voltage and frequency
-scaling (DVFS) technique, through reducing the frequency of a CPU. Using DVFS to lower levels
-result in a high increase in performance degradation ratio. Therefore selecting the best frequencies
-must give the best possible tradeoff between the energy and the performance of parallel program.
+Computing platforms are consuming more and more energy due to the increase of the number of nodes composing them. To minimize the operating costs of these platforms many techniques have been used. Dynamic voltage and frequency scaling (DVFS) is one of them, it reduces the frequency of a CPU to lower its energy consumption. However, lowering the frequency of a CPU might increase the execution time of an application running on that processor. Therefore, the frequency that gives the best tradeoff between the energy consumption and the performance of an application must be selected.
-In this paper we present a new online heterogeneous scaling algorithm that selects the best vector
-of frequency scaling factors. These factors give the best tradeoff between the energy saving and the
-performance degradation. The algorithm has small overhead and works without training and profiling.
-We developed a new energy model for distributed iterative application running on heterogeneous cluster.
-The proposed algorithm experimented on Simgrid simulator that applying the NAS parallel benchmarks.
-It reduces the energy consumption up to 35\% while limits the performance degradation as much as possible.
+In this paper, a new online frequencies selecting algorithm for heterogeneous platforms is presented. It selects the frequency that gives the best tradeoff 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 platform.
+The proposed algorithm was evaluated on the Simgrid simulator while running the NAS parallel benchmarks.
+The experiments demonstrated that it reduces the energy consumption up to 35\% while limiting the performance degradation as much as possible.
\end{abstract}
\section{Introduction}
\label{sec.intro}
-Modern processors continue increasing in performance,
-the CPUs constructors are competing to achieve maximum number
-of floating point operations per second (FLOPS).
-Thus, the energy consumption and the heat dissipation are increased
-drastically according to this increase. Because the number of FLOPS
-is more related to the power consumption of a CPU
-~\cite{Luley_Energy.efficiency.evaluation.and.benchmarking}.
-As an example of the most power hungry cluster, Tianhe-2 became in
-the top of the Top500 list in June 2014 \cite{TOP500_Supercomputers_Sites}.
-It has more than 3 millions of cores and consumed more than 17.8 megawatts.
+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 platform might achieve higher floating point operations per second (FLOPS), but the energy consumption and the heat dissipation are also increased. As an example, the chinese supercomputer Tianhe-2 had the highest FLOPS in November 2014 according to the Top500 list \cite{TOP500_Supercomputers_Sites}. However, it was also the most power hungry platform with its over 3 millions cores consuming around 17.8 megawatts.
Moreover, according to the U.S. annual energy outlook 2014
\cite{U.S_Annual.Energy.Outlook.2014}, the price of energy for 1 megawatt-hour
was approximately equal to \$70.
-Therefore, we can consider the price of the energy consumption for the
-Tianhe-2 platform is approximately more than \$10 millions for
-one year. For this reason, the heterogeneous clusters must be offer more
-energy efficiency due to the increase in the energy cost and the environment
-influences. Therefore, a green computing clusters with maximum number of
-FLOPS per watt are required nowadays. For example, the GSIC center of Tokyo,
+Therefore, the price of the energy consumed by the
+Tianhe-2 platform is approximately more than \$10 millions each year.
+The computing platforms must be more energy efficient and offer the highest number of FLOPS per watt possible, such as the TSUBAME-KFC at the GSIC center of Tokyo which
became the top of the Green500 list in June 2014 \cite{Green500_List}.
-This heterogeneous platform has more than four thousand of MFLOPS per watt. Dynamic
-voltage and frequency scaling (DVFS) is a process used widely to reduce the energy
-consumption of the processor. In heterogeneous clusters enabled DVFS, many researchers
-used DVFS in a different ways. DVFS can be minimized the energy consumption
-but it leads to a disadvantage due to the increase in performance degradation.
-Therefore, researchers used different optimization strategies to overcame
-this problem. The best tradeoff relation between the energy reduction and
-performance degradation ratio is became a key challenges in a heterogeneous
-platforms. In this paper we are propose a heterogeneous scaling algorithm
-that selects the optimal vector of the frequency scaling factors for distributed
-iterative application, producing maximum energy reduction against minimum
-performance degradation ratio simultaneously. The algorithm has very small
-overhead, works online and not needs for any training or profiling.
+This heterogeneous platform executes more than four GFLOPS per watt.
+
+ Besides hardware improvements, there are many software techniques to lower the energy consumption of these platforms, such as scheduling, DVFS, ... DVFS is a widely used process to reduce the energy
+consumption of a processor by lowering its frequency. \textbf{put a reference to DVFS} However, it also the reduces the number of FLOPS executed by the processor which might increase the execution time of the application running over that processor.
+Therefore, researchers used different optimization strategies to select the frequency that gives the best tradeoff between the energy reduction and
+performance degradation ratio.
+\textbf{you should talk about the first paper here and say that the algorithm was applied to a homogeneous platform then define what is a heterogeneous platform, you can take it from the firdt paragraph in section 3 }
+
+In this paper, a frequency selecting algorithm is proposed. It selects the vector of frequencies for a heterogeneous platform that runs a message passing iterative application, that gives the maximum energy reduction and minimum
+performance degradation ratio simultaneously. The algorithm has a very small
+overhead, works online and does not need any training or profiling.
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 MPI programs can be predicted. It also presents an energy
-model for heterogeneous platforms. Section~\ref{sec.compet} presents
+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 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 heterogeneous scaling algorithm.
-Section~\ref{sec.expe} presents the results of running the NAS benchmarks on
-the proposed heterogeneous platform. It also shows the comparison of three
-different power scenarios and it verifies the precision of the proposed algorithm.
+Section~\ref{sec.optim} details the proposed frequency selecting algorithm then the precision of the proposed algorithm is verified.\textbf{the verification should be put here}
+Section~\ref{sec.expe} presents the results of applying the algorithm on the NAS parallel benchmarks and executing them
+on a heterogeneous platform. It also shows the results of running three
+different power scenarios and comparing them.
Finally, we conclude in Section~\ref{sec.concl} with a summary and some future works.
+\textbf{never use we in an article and the algorithm is not heterogeneous! you cannot use scaling factors before defining what they are.}
\section{Related works}
\label{sec.relwork}
Energy reduction process for high performance clusters recently performed using
\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}
+
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