-\AG{Consider introducing the models sec.~\ref{sec.exe} maybe before related works}
-
-In the this section some heuristics to compute the scaling factor are
-presented and classified in two parts: offline and online methods.
-
-\subsection{The offline DVFS orientations}
-
-The DVFS offline methods are static and are not executed during the runtime of
-the program. Some approaches used heuristics to select the best DVFS state
-during the compilation phases as for example in Azevedo et al.~\cite{40}. They
-use dynamic voltage scaling (DVS) algorithm to choose the DVS setting when there
-are dependency points between tasks. While in~\cite{29}, Xie et al. used
-breadth-first search algorithm to do that. Their goal is to save energy with
-time limits. Another approach gathers and stores the runtime information for
-each DVFS state, then selects the suitable DVFS offline to optimize energy-time
-trade offs. As an example, Rountree et al.~\cite{8} use liner programming
-algorithm, while in~\cite{38,34}, Cochran et al. use multi logistic regression
-algorithm for the same goal. The offline study that shows the DVFS impact on the
-communication time of the MPI program is~\cite{17}, where Freeh et al. show that
-these times do not change when the frequency is scaled down.
-
-\subsection{The online DVFS orientations}
-
-The objective of the online DVFS orientations is to dynamically compute and set
-the frequency of the CPU for saving energy during the runtime of the
-programs. Estimating and predicting approaches for the energy-time trade offs
-are developed by Kimura, Peraza, Yu-Liang et al. ~\cite{11,2,31}. These works
-select the best DVFS setting depending on the slack times. These times happen
-when the processors have to wait for data from other processors to compute their
-task. For example, during the synchronous communications that take place in MPI
-programs, some processors are idle. The optimal DVFS can be selected using
-learning methods. Therefore, in Dhiman, Hao Shen et al. ~\cite{39,19} used
-machine learning to converge to the suitable DVFS configuration. Their learning
-algorithms take big time to converge when the number of available frequencies is
-high. Also, the communication sections of the MPI program can be used to save
-energy. In~\cite{1}, Lim et al. developed an algorithm that detects the
+\AG{Consider introducing the models (sec.~\ref{sec.exe}) before related works}
+
+In this section, some heuristics to compute the scaling factor are
+presented and classified into two categories: offline and online methods.
+
+\subsection{Offline scaling factor selection methods}
+
+The offline scaling factor selection methods are executed before the runtime of
+the program. They return static scaling factor values to the processors
+participating in the execution of the parallel program. On one hand, the scaling
+factor
+values could be computed based on information retrieved by analyzing the code of
+the program and the computing system that will execute it. In ~\cite{40},
+Azevedo et
+al. detect during compilation the dependency points between
+tasks in a parallel program. This information is then used to lower the frequency of
+some processors in order to eliminate slack times. A slack time is the period of time during which a processor that have already finished its computation, have to wait
+for a set of processors to finish their computations and send their results to the
+waiting processor in order to continue its task that is
+dependent on the results of computations being executed on other processors.
+Freeh et al. showed in ~\cite{17} that the
+communication times of MPI programs do not change when the frequency is scaled down.
+On the other hand, some offline scaling factor selection methods use the
+information gathered from previous full or
+partial executions of the program. A part or the whole program is usually executed over all the available frequency gears and the the execution time and the energy consumed with each frequency gear are measured. Then an heuristic or an exact method uses the retrieved information to compute the values of the scaling factor for the processors.
+ In~\cite{29}, Xie et al. use an exact exponential breadth-first search algorithm to compute the scaling factor values that give the optimal energy reduction while respecting a deadline for a sequential program. They also present a linear heuristic that approximates the optimal solution. In~\cite{8} , Rountree et al. use a linear programming
+algorithm, while in~\cite{38,34}, Cochran et al. use multi logistic regression algorithm for the same goal.
+The main drawback for these methods is that they all require executing a part or the whole program on all frequency gears for each new instance of the same program.
+
+\subsection{Online scaling factor selection methods}
+The online scaling factor selection methods are executed during the runtime of the program. They are usually integrated into iterative programs where the same block of instructions is executed many times. During the first few iterations, many informations are measured such as the execution time, the energy consumed using a multimeter, the slack times, ... Then a method will exploit these measurements to compute the scaling factor values for each processor. This operation, measurements and computing new scaling factor, can be repeated as much as needed if the iterations are not regular. Kimura, Peraza, Yu-Liang et al. ~\cite{11,2,31} used learning methods to select the appropriate scaling factor values to eliminate the slack times during runtime. However, as seen in ~\cite{39,19}, machine learning methods can take a lot of time to converge when the number of available gears is big. To reduce the impact of slack times, in~\cite{1}, Lim et al. developed an algorithm that detects the
