+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
+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 a
+synchronous and an asynchronous version of the MS application. Both the simulation
+and real experiment results show that applying synchronous frequency selecting algorithm on an
+asynchronous MS application gives the best tradeoff between energy consumption reduction
+and performance compared to other scenarios. In the simulation results, this scenario
+saves on average the energy consumption by 22\% and reduces 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 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 dynamic and static powers values. More energy reduction has been achieved when the ratio of the
+dynamic power have been increase and vice versa. whereas, the performance degradation percentages were decreased when the static power ratio has been increased.
+In the Grid’5000 results, this scenario saves the energy consumption by 26.93\% and
+reduces 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 Simulation and Grid'5000 testbed experiments, we have compared the proposed algorithm 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.
+Finally, we outline some perspectives that will be applied to this work in the future as in the next section.
