modifications for the related work

diff --git a/Heter_paper.tex b/Heter_paper.tex
index e37939faffe596ff34eb300e8f6d42e7c7d39655..78ca9987e439e38604e4c67620b62190d75dbead 100644 (file)
--- a/Heter_paper.tex
+++ b/Heter_paper.tex
@@ -164,8 +164,8 @@ Many methods were conceived to reduce the energy consumption of this type of pla
 developed a method that minimizes the value of $energy*delay^2$ (the delay is the sum of slack times that happen during synchronous communications) by dynamically assigning new frequencies to the CPUs of the heterogeneous cluster.. Lizhe et al.~\cite{Lizhe_Energy.aware.parallel.task.scheduling} propose
 an algorithm that divides the executed tasks into two types: the critical and 
 non critical tasks. The algorithm scales down the frequency of  non critical tasks proportionally to their  slack and communication times while limiting  the performance degradation percentage to less than 10\%. In~\cite{Joshi_Blackbox.prediction.of.impact.of.DVFS} 
 developed a method that minimizes the value of $energy*delay^2$ (the delay is the sum of slack times that happen during synchronous communications) by dynamically assigning new frequencies to the CPUs of the heterogeneous cluster.. Lizhe et al.~\cite{Lizhe_Energy.aware.parallel.task.scheduling} propose
 an algorithm that divides the executed tasks into two types: the critical and 
 non critical tasks. The algorithm scales down the frequency of  non critical tasks proportionally to their  slack and communication times while limiting  the performance degradation percentage to less than 10\%. In~\cite{Joshi_Blackbox.prediction.of.impact.of.DVFS} 

-and \cite{Spiliopoulos_Green.governors.Adaptive.DVFS},  a heterogeneous cluster composed of two  types 
-of Intel and AMD processors. The consumed energy  and the performance  were measured for each frequency, then a linear regression method is used to select the gear that gave the best tradeoff between energy consumption and performance. 
+  a heterogeneous cluster composed of two  types 
+of Intel and AMD processors. They use a gradient method to predict the impact of DVFS operations on performance.

 In~\cite{Shelepov_Scheduling.on.Heterogeneous.Multicore} and \cite{Li_Minimizing.Energy.Consumption.for.Frame.Based.Tasks}, 
  the best frequencies for a specified heterogeneous cluster are selected offline using some 
 heuristic. Chen et al.~\cite{Chen_DVFS.under.quality.of.service.requirements} used a greedy dynamic programming approach to  
 In~\cite{Shelepov_Scheduling.on.Heterogeneous.Multicore} and \cite{Li_Minimizing.Energy.Consumption.for.Frame.Based.Tasks}, 
  the best frequencies for a specified heterogeneous cluster are selected offline using some 
 heuristic. Chen et al.~\cite{Chen_DVFS.under.quality.of.service.requirements} used a greedy dynamic programming approach to