-In chapter \ref{ch2}, we were proposed a new online scaling factor selection method that optimized simultaneously the energy and performance of a distributed synchronous application running on a homogeneous cluster. We have applied this algorithm to the NAS benchmarks of the class C and evaluated by the SimGrid simulator. Firstly, Rauber and Rünger’s energy model used by the proposed algorithm to select best frequency gear. The proposed algorithm was compared to the Rauber and Rünger optimization method, which gives better energy and performance trade-off ratios compare to them. Secondly, a new energy consumption model was developed to take into consideration both the computation and communication times and their relation with the frequency scaling factor. The new enenrgy model was used by the proposed algorithm to select different frequencies. Thus, a new simulation results have been shown, which are more accurate and realistic than other results obtained using the Rauber and Rünger's energy model.
-In chapter \ref{ch3}, we were proposed two new online frequency scaling factors selecting algorithms to select the best possible vectors of frequency scaling factors that give best trade-off between the predicted energy and the predicted performance values of synchronous iterative application running over a heterogeneous cluster and a grid. Each algorithm depends on a new energy and performance models, which takes into account the underline parallel platform being used. Firstly, the proposed
-scaling factors selection algorithm for a heterogeneous local cluster was implemented to the NAS parallel benchmarks of the class C and simulated by SimGrid. The results of the experiments showed that the algorithm on average reduces by 29.8\% the energy consumption of the NAS benchmarks executed over 8 nodes while limiting the degradation of the performance by 3.8\%.
-Different frequency scaling factors were selected by the algorithm according to the ratio between the computation and communication times when different number of nodes were used, and when different values have been used for static and dynamic powers of the CPU. Secondly, the proposed scaling factors selection algorithm for a grid was implemented to the NAS parallel benchmarks of the class D and executed over Grid5000 testbed platform. The experiments on 16 nodes, distributed over three clusters, showed that the algorithm on average reduces by 30\% the energy consumption for all the NAS benchmarks while on average only degrading by 3.2\% the performance.
-The algorithm was also evaluated in different scenarios that vary in the distribution of the computing nodes between different clusters’ sites or use multi-cores per node architecture or consume different static power values. The algorithm selects different vectors of frequencies according to the computations and communication times ratios, and the values of the static and measured dynamic powers of the CPUs.
-Both of the proposed algorithms were compared to another method that uses the well known energy and delay product as an objective function. The comparison results showed that the proposed algorithms outperform the latter by selecting a vectors of frequencies that give a better trade-off between energy consumption reduction and performance.
-In chapter \ref{ch4}, we were presented a new online frequency selection algorithm for asynchronous iterative applications running over a grid. The algorithm uses new energy and performance models to predict the energy consumption and the execution time of asynchronous or hybrid message passing
+In chapter \ref{ch2}, a new online scaling factor selection method that optimizes simultaneously the energy and performance of a distributed synchronous application with iterations running on a homogeneous cluster has been proposed. This algorithm was applied to the NAS benchmarks of the class C and executed over the SimGrid simulator. Firstly, Rauber and Rünger’s energy model was used in the proposed algorithm to select the best frequency gear. The proposed algorithm was compared to the Rauber and Rünger's optimization method. The results of the comparison showed that the proposed algorithm gives better energy to performance trade-off ratios compared to their methods while using the same energy model. Secondly, a new energy consumption model was developed to take into consideration both the computation and communication times and their relation with the frequency scaling factor. The new energy model was used by the proposed algorithm. The new simulation results demonstrated that the new model is more accurate and realistic than the previous one.
+
+In chapter \ref{ch3}, two new online frequency scaling factors selecting algorithms adapted for synchronous application with iterations running over a heterogeneous cluster and a grid were presented. Each algorithm uses new energy and performance models which take into account the characteristics of the parallel platform being used. Firstly, the proposed
+scaling factors selection algorithm for a heterogeneous local cluster was applied to the NAS parallel benchmarks and evaluated over SimGrid. The results of the experiments showed that the algorithm on average reduces by 29.8\% the energy consumption of the class C of the NAS benchmarks executed over 8 nodes while limiting the degradation of the performance to 3.8\%.
+Different frequency scaling factors were selected by the algorithm according to the ratio between the computation and communication times when different number of nodes were used, and when different static and dynamic CPU powers have been used. Secondly, the proposed scaling factors selection algorithm for a grid was applied to the NAS parallel benchmarks and the class D of these benchmarks was executed over the Grid5000 testbed platform. The experiments conducted over 16 nodes distributed over three clusters, showed that the algorithm on average reduces by 30\% the energy consumption for all the NAS benchmarks while on average only degrading by 3.2\% their performance.
+The algorithm was also evaluated in different scenarios that vary in the distribution of the computing nodes between different clusters’ sites or use multi-cores per node architectures or consume different static power values. The algorithm selects different vectors of frequencies according to the computations and communication times ratios, and the values of the static and measured dynamic powers of the CPUs.
+Both of the proposed algorithms were compared to another method that uses the well known energy and delay product as an objective function. The comparison results showed that the proposed algorithms outperform the latter by selecting vectors of frequencies that give a better trade-off between energy consumption reduction and performance.
+
+In chapter \ref{ch4}, a new online frequency selection algorithm were adapted for asynchronous iterative applications running over a grid was presented. The algorithm uses new energy and performance models to predict the energy consumption and the execution time of asynchronous or hybrid message passing
