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
