of iterative methods, which is presented in the following sections.
-\subsection{Energy model for heterogeneous platform}
+\subsection{Energy model for heterogeneous grid platform}
Many researchers~\cite{Malkowski_energy.efficient.high.performance.computing,
Rauber_Analytical.Modeling.for.Energy,Zhuo_Energy.efficient.Dynamic.Task.Scheduling,
are less by approximately double, linear speed-up, for most of the benchmarks comparing to the one site with 16 nodes scenario.
This leads to increased the number of the critical nodes which any one of them may increased the overall the execution time of the benchmarks.
The EP benchmarks is gives the bigger performance degradation ratio, because there is no
-communications and no slack times in this benchmarks which their performance govern
+communications and no slack times in this benchmarks which their performance controlled by
+the computing powers of the nodes.
The tradeoff between these scenarios can be computed as in the tradeoff function \ref{eq:max}.
Figure \ref{fig:dist}, presents the tradeoff distance for all benchmarks over all
platform scenarios. The one site scenario with 16 and 32 nodes had the best tradeoff distance
