adding the abstract

diff --git a/Heter_paper.tex b/Heter_paper.tex
index 2fc854959ff4a138331b9011b0833c60010fe89b..65232f04f870ef03b4b48f7e9cf6cc99481360b1 100644 (file)
--- a/Heter_paper.tex
+++ b/Heter_paper.tex
@@ -76,7 +76,19 @@
 \maketitle
 
 \begin{abstract}
 \maketitle
 
 \begin{abstract}

-  
+Green computing emphasizes the importance of energy conservation, minimizing the negative impact 
+on the environment while achieving high performance and minimizing operating costs. So, energy reduction 
+process in a high performance clusters it can be archived using dynamic voltage and frequency 
+scaling (DVFS) technique, through reducing the frequency of a CPU. Using DVFS to lower levels 
+result in a high increase in performance degradation ratio. Therefore selecting the best frequencies 
+must give the best possible tradeoff between the energy and the performance of parallel program.
+
+In this paper we present a new online heterogeneous scaling algorithm that selects the best vector 
+of frequency scaling factors. These factors give the best tradeoff between the energy saving and the
+performance degradation. The algorithm has small overhead and works without training and profiling.
+We developed a new energy model for distributed iterative application running on heterogeneous cluster. 
+The proposed algorithm experimented  on Simgrid simulator that applying the NAS parallel benchmarks.
+It reduces the energy consumption up to 35\% while limits the performance degradation as much as possible.

 \end{abstract}
 
 \section{Introduction}
 \end{abstract}
 
 \section{Introduction}


@@ -153,9 +165,9 @@ on  CPUs. As an example of this works, Luley et al.
 cluster composed of Intel Xeon CPUs and NVIDIA GPUs. Their main goal is to determined the 
 energy efficiency as a function of performance per watt, the best tradeoff is done when the 
 performance per watt function is maximized. In the work of Kia Ma et al.
 cluster composed of Intel Xeon CPUs and NVIDIA GPUs. Their main goal is to determined the 
 energy efficiency as a function of performance per watt, the best tradeoff is done when the 
 performance per watt function is maximized. In the work of Kia Ma et al.

-~\cite{KaiMa_Holistic.Approach.to.Energy.Efficiency.in.GPU-CPU}, They developed a scheduling 
+~\cite{KaiMa_Holistic.Approach.to.Energy.Efficiency.in.GPU-CPU}, they developed a scheduling 

 algorithm to distributed different workloads proportional to the computing power of the node 
 algorithm to distributed different workloads proportional to the computing power of the node 

-to be executed on a CPU or a GPU, emphasize all tasks must be finished in the same time. 
+to be executed on CPU or GPU, emphasize all tasks must be finished in the same time. 

 Recently, Rong et al.~\cite{Rong_Effects.of.DVFS.on.K20.GPU}, Their study explain that 
 a heterogeneous clusters enabled DVFS using GPUs and CPUs gave better energy and performance 
 efficiency than other clusters composed of only CPUs. 
 Recently, Rong et al.~\cite{Rong_Effects.of.DVFS.on.K20.GPU}, Their study explain that 
 a heterogeneous clusters enabled DVFS using GPUs and CPUs gave better energy and performance 
 efficiency than other clusters composed of only CPUs.