+
+\begin{abstract}
+
+ In recent years, green computing topic has being became an important topic in
+ the domain of the research. The increase in computing power of the computing
+ platforms is increased the energy consumption and the carbon dioxide emissions.
+ Many techniques have being used to minimize the cost of the energy consumption
+ and reduce environmental pollution. Dynamic voltage and frequency scaling (DVFS)
+ is one of these techniques. It used to reduce the power consumption of the CPU
+ while computing by lowering its frequency. Moreover, lowering the frequency of
+ a CPU may increase the execution time of an application running on that
+ processor. Therefore, the frequency that gives the best trade-off between
+ the energy consumption and the performance of an application must be selected.
+
+ In this paper, a new online frequency selecting algorithm for heterogeneous
+ grid (heterogeneous CPUs) is presented. It selects the frequencies and tries to give the best
+ trade-off between energy saving and performance degradation, for each node
+ computing the message passing iterative application. The algorithm has a small
+ overhead and works without training or profiling. It uses a new energy model
+ for message passing iterative applications running on a heterogeneous
+ grid. The proposed algorithm is evaluated on real testbed, grid'5000 platform, while
+ running the NAS parallel benchmarks. The experiments show that it reduces the
+ energy consumption on average up to \np[\%]{30} while declines the performance
+ on average by \np[\%]{3} only for the same instance. Finally, the algorithm is
+ compared to an existing method, the comparison results show that it outperforms the
+ latter in term of energy and performance trade-off.
+\end{abstract}
+
+
