+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
+iterative applications running over a grid. The proposed algorithm was evaluated twice
+over the SimGrid simulator and Grid’5000 testbed while running a multi-splitting (MS)
+application that solves 3D problems. The experiments were executed over different grid
+scenarios composed of different numbers of clusters and different numbers of nodes
+per cluster. The proposed algorithm was applied synchronously and asynchronously on
+synchronous and asynchronous versions of the MS iterative application. Both the simulations
+and real experiments results showed that applying synchronously the frequency selecting algorithm on an
+asynchronous MS application gives the best tradeoff between energy consumption reduction
+and performance when compared to the other scenarios. In the simulation results, this scenario
+reduces on average the energy consumption by 22\% and decreases the execution time of
+the application by 5.72\%. This version optimizes both of the dynamic energy
+consumption by applying synchronously the HSA algorithm at the end of the first iteration of the iterative application and the static energy consumption by using asynchronous communications between nodes from
+different clusters which are overlapped by computations. The proposed algorithm was also
+evaluated over three power scenarios which selects different vectors of frequencies proportionally to the dynamic and static powers values. More energy reduction was achieved when the ratio of the
+dynamic power was increased and vice versa. Whereas, the performance degradation percentages were decreased when the static power ratio was increased.
+In the Grid’5000 experiments, this scenario reduces the energy consumption by 26.93\% and
+decreases the execution time of the application by 21.48\%. The experiments executed over Grid'5000 give better results than those simulated with SimGrid because the nodes used in Grid'5000 were more heterogeneous than the ones simulated by SimGrid.
+In both of the Simulations and real experiments, the proposed algorithm was compared to a method that uses the well known energy and delay product as an objective function. The comparison results showed that the proposed algorithm outperforms the latter by selecting a vector of frequencies that gives
+a better trade-off between the energy consumption reduction and the performance.
