-In this section, we compare our scaling factors selection algorithm
-with Spiliopoulos et al. algorithm \cite{Spiliopoulos_Green.governors.Adaptive.DVFS}.
-They developed an online frequency selecting algorithm running over multicore architecture.
-The algorithm predicted both the energy and performance during the runtime of the program, then
-selecting the frequencies that minimized the energy and delay products (EDP), $EDP=Enegry*Delay$.
-To be able to compare with this algorithm, we used our energy and execution time models in prediction process,
-equations (\ref{eq:energy}) and (\ref{eq:fnew}). Also their algorithm is adapted to taking into account
-the heterogeneous platform to starts selecting the
-initial frequencies using the equation (\ref{eq:Fint}). The algorithm built to test all possible frequencies as
-a brute-force search algorithm.
-
-The comparison results of running NAS benchmarks class C on 8 or 9 nodes are
-presented in table \ref{table:compare_EDP}. The results show that our algorithm has a biggest energy saving percentage,
-on average it has 29.76\% and thier algorithm has 25.75\%,
-while the average of performance degradation percentage is approximately the same, the average for our algorithm is
-equal to 3.89\% and for their algorithm is equal to 4.03\%. In general, our algorithm outperforms
-Spiliopoulos et al. algorithm in term of energy and performance tradeoff see figure (\ref{fig:compare_EDP}).
-This because our algorithm maximized the difference (the distance) between the energy saving and the performance degradation
-comparing to their EDP optimization function. It is also keeps the frequency of the slowest node without change
-that gave some enhancements to the energy and performance tradeoff.
+In this section, the scaling factors selection algorithm
+is compared to Spiliopoulos et al. algorithm \cite{Spiliopoulos_Green.governors.Adaptive.DVFS}.
+They developed a green governor that regularly applies an online frequency selecting algorithm to reduce the energy consumed by a multicore architecture without degrading much its performance. The algorithm selects the frequencies that minimize the energy and delay products, $EDP=Enegry*Delay$ using the predicted overall energy consumption and execution time delay for each frequency.
+ To fairly compare both algorithms, the same energy and execution time models, equations (\ref{eq:energy}) and (\ref{eq:fnew}), were used for both algorithms to predict the energy consumption and the execution times. Also Spiliopoulos et al. algorithm was adapted to start the search from the
+initial frequencies computed using the equation (\ref{eq:Fint}). The resulting algorithm is an exhaustive search algorithm that minimizes the EDP and has the initial frequencies values as an upper bound.
+
+Both algorithms were applied to the parallel NAS benchmarks to compare their efficiency. Table \ref{table:compare_EDP} presents the results of comparing the execution times and the energy consumptions for both versions of the NAS benchmarks while running the class C of each benchmark over 8 or 9 heterogeneous nodes. The results show that our algorithm gives better energy savings than Spiliopoulos et al. algorithm,
+on average it results in 29.76\% energy saving while their algorithm returns just 25.75\%. The average of performance degradation percentage is approximately the same for both algorithms, about 4\%.
+
+For all benchmarks, our algorithm outperforms
+Spiliopoulos et al. algorithm in term of energy and performance tradeoff, see figure (\ref{fig:compare_EDP}) because it maximizes the distance between the energy saving and the performance degradation values while giving the same weight for both metrics.
