Index: papers/SMPaT-2012_DCWoRMS/elsarticle-DCWoRMS.tex
===================================================================
--- papers/SMPaT-2012_DCWoRMS/elsarticle-DCWoRMS.tex	(revision 719)
+++ papers/SMPaT-2012_DCWoRMS/elsarticle-DCWoRMS.tex	(revision 720)
@@ -426,5 +426,5 @@
 
 \begin {table}[h!]
-
+\centering
 \begin{tabular}{llr}
 \hline
@@ -436,9 +436,9 @@
 Atom D510 64 Bit & 2 GB &	4 \\
 \hline
-\multicolumn{3}{c}{Storage} \\
-Type & Size & Connection  \\
-\hline
-Storage Head 520 & 16 x 300 GB SSD & 2 x 10 Gbit/s CX4 \\
-\hline
+%\multicolumn{3}{c}{Storage} \\
+%Type & Size & Connection  \\
+%\hline
+%Storage Head 520 & 16 x 300 GB SSD & 2 x 10 Gbit/s CX4 \\
+%\hline
 \end{tabular}
 \caption {\label{testBed} RECS system configuration}
@@ -471,5 +471,5 @@
 
 \begin {table}[ tp]
-
+\centering
 \begin{tabular}{l c c c c r}
 \hline
@@ -529,5 +529,5 @@
 \end{figure}
 
-In this version of experiment we neglected additional cost and time necessary to change the power state of resources. As can be observed in the power consumption chart in the Figure~\ref{fig:70rnpm}, switching of unused nodes led to decrease of the total energy consumption. As expected, with respect to the makespan criterion, both approaches perform equally reaching \textbf{workload completion time}: 533 820 s. However, the pure random strategy was significantly outperformed in terms of energy usage, by the policy with additional node power management with it \textbf{total energy usage}: 36,705 kWh. The overall energy savings reached 22\%. 
+In this version of experiment we neglected additional cost and time necessary to change the power state of resources. As can be observed in the power consumption chart in the Figure~\ref{fig:70rnpm}, switching of unused nodes led to decrease of the total energy consumption. As expected, with respect to the makespan criterion, both approaches perform equally reaching \textbf{workload completion time}: 533 820 s. However, the pure random strategy was significantly outperformed in terms of energy usage, by the policy with additional node power management with its \textbf{total energy usage}: 36,705 kWh. The overall energy savings reached 22\%. 
 
 \subsubsection{Energy optimization}
@@ -549,5 +549,5 @@
 \end {table}
 
-As mentioned, we assign tasks to nodes minimizing the value of expression: $(P-Pidle)*exec\_time$, where $P$ denotes observed power of the node running the particular application and $exec\_time$ refers to the measured application running time. Based on the application and hardware profiles, we expected that Atom D510 would be the preferred node. Obtained scheduled, that is presented in the Gantt chart in Figure~\ref{fig:70eo} along with the energy and system usage, confirmed our assumptions. Atom D510 nodes are nearly fully loaded, while the least energy-favorable AMD nodes are used only when other ones are busy.
+As mentioned, we assign tasks to nodes minimizing the value of expression: $(P-Pidle)*exec\_time$, where $P$ denotes observed power of the node running the particular application and $exec\_time$ refers to the measured application running time. Based on the application and hardware profiles, we expected that Atom D510 would be the preferred node. Obtained schedule, that is presented in the Gantt chart in Figure~\ref{fig:70eo} along with the energy and system usage, confirmed our assumptions. Atom D510 nodes are nearly fully loaded, while the least energy-favourable AMD nodes are used only when other ones are busy.
 
 \begin{figure}[h!]
@@ -560,6 +560,6 @@
 
 
-The next strategy is similar to the previous one, so making the assignment of task to the node, we still take into consideration application and hardware profiles, but in that case we assume that the system supports possibility of switching off unused nodes. In this case the minimal energy consumption is achieved by assigning the task to the node for which the product of power consumption and time of execution is minimal. In other words we minimized the following expression: "$P*exec\_time$.
-Contrary to the previous strategy, we expected Intel I7 nodes to be allocated first. Generated Gantt chart is compatible with our expectations.
+The next strategy is similar to the previous one, so making the assignment of task to the node, we still take into consideration application and hardware profiles, but in that case we assume that the system supports possibility of switching off unused nodes. In this case the minimal energy consumption is achieved by assigning the task to the node for which the product of power consumption and time of execution is minimal. In other words we minimized the following expression: $P*exec\_time$.
+Contrary to the previous strategy, we expected Intel I7 nodes to be allocated first. Generated Gantt chart is consistent with our expectations.
 
 \begin{figure}[h!]
@@ -569,5 +569,5 @@
 \end{figure}
 
-Estimated \textbf{total energy usage} of the system is 30,568 kWh. As we can see, this approach significantly improved the value of this criterion, comparing to the previous policies. Moreover, the proposed allocation strategy does not worsen the \textbf{workload completion time} criterion, where the resulting value is equal to 533 820 s.
+Estimated \textbf{total energy usage} of the system is 30,568 kWh. As we can see, this approach significantly improved the value of this criterion, comparing to the previous policies. Moreover, the proposed allocation strategy does not worsen the \textbf{workload completion time} criterion, for which the resulting value is equal to 533 820 s.
 
 \subsubsection{Frequency scaling}
@@ -583,4 +583,13 @@
 
 As we were looking for the trade-off between total completion time and energy usage, we were searching for the workload load level that can benefit from the lower system performance in terms of energy-efficiency. For the frequency downgrading policy, we observed the improvement on the energy usage criterion only for the workload resulting in 10\% system load.
+
+Figure~\ref{fig:dfsComp} shows schedules obtained for Random and DFS strategy. One should easily note that the
+\begin{figure}[h!]
+\centering
+\includegraphics[width = 12cm]{fig/dfsComp.png}
+\caption{\label{fig:dfsComp} Schedules obtained for Random strategy (left) and DFS strategy (right) for 10\% of system load}
+\end{figure}
+
+
 The following tables: Table~\ref{loadEnergy} and Table~\ref{loadMakespan} contain the values of evaluation criteria (total energy usage and makespan respectively) gathered for all investigated workloads.
 
@@ -598,5 +607,5 @@
 \hline
 \end{tabular}
-\caption {\label{loadEnergy} Energy usage [kWh] for different level of system load}
+\caption {\label{loadEnergy} Energy usage [kWh] for different level of system load. R - Random, R+NPM - Random + node power management, EO - Energy optimization, EO+NPM - Energy optimization + node power management, DFS - Dynamic Frequency Scaling}
 \end {table}
 
@@ -614,6 +623,10 @@
 \hline
 \end{tabular}
-\caption {\label{loadMakespan} Makespan [s] for different level of system load}
+\caption {\label{loadMakespan} Makespan [s] for different level of system load. R - Random, R+NPM - Random + node power management, EO - Energy optimization, EO+NPM - Energy optimization + node power management, DFS - Dynamic Frequency Scaling}
 \end {table}
+
+One should easily note that gain from switching off unused nodes decreases with the increasing workload density. In general, for the highly loaded system such policy does not find an application due to the cost related to this process and relatively small benefits.
+
+...
 
 \section{DCWoRMS application/use cases}\label{sec:coolemall}
