Changeset 1075 for papers/SMPaT-2012_DCWoRMS

Timestamp:

06/03/13 18:36:18 (12 years ago)

Author:

wojtekp

Message:

 

Location:

papers/SMPaT-2012_DCWoRMS

Files:

• elsarticle-DCWoRMS.aux (modified) (2 diffs)
• elsarticle-DCWoRMS.fdb_latexmk (modified) (2 diffs)
• elsarticle-DCWoRMS.pdf (modified) (previous)
• elsarticle-DCWoRMS.synctex.gz (modified) (previous)
• elsarticle-DCWoRMS.tex (modified) (4 diffs)

papers/SMPaT-2012_DCWoRMS/elsarticle-DCWoRMS.aux

@@ -26 +26 @@
 \newlabel{sota}{{2}{3}}
 \citation{GSSIM}
+\@writefile{toc}{\contentsline {section}{\numberline {3}DCworms}{5}}
 \citation{GSSIM}
 \@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces  DCworms architecture}}{6}}
 \newlabel{fig:arch}{{1}{6}}
-\@writefile{toc}{\contentsline {section}{\numberline {3}DCworms}{6}}
 \@writefile{toc}{\contentsline {subsection}{\numberline {3.1}Architecture}{6}}
 \citation{GWF}
@@ -45 +45 @@
 \@writefile{toc}{\contentsline {paragraph}{\textbf  {Power profile}}{10}}
 \@writefile{toc}{\contentsline {paragraph}{\textbf  {Power consumption model}}{10}}
+\@writefile{toc}{\contentsline {paragraph}{\textbf  {Power management interface}}{10}}
 \@writefile{lof}{\contentsline {figure}{\numberline {3}{\ignorespaces  Power consumption modeling}}{11}}
 \newlabel{fig:powerModel}{{3}{11}}
-\@writefile{toc}{\contentsline {paragraph}{\textbf  {Power management interface}}{11}}
 \@writefile{toc}{\contentsline {subsection}{\numberline {3.5}Application performance modeling}{11}}
 \newlabel{sec:apps}{{3.5}{11}}

papers/SMPaT-2012_DCWoRMS/elsarticle-DCWoRMS.fdb_latexmk

@@ -1 +1 @@
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papers/SMPaT-2012_DCWoRMS/elsarticle-DCWoRMS.tex

@@ -48 +48 @@
 \usepackage{multirow}
 %% The amsthm package provides extended theorem environments
-%% \usepackage{amsthm}
+%% \usepackage{amsthm}  
 
 %% The lineno packages adds line numbers. Start line numbering with
@@ -165 +165 @@
 \section{Introduction}
 
-Rising popularity of large-scale computing infrastructures caused quick development of data centers. Nowadays, data centers are responsible for around 2\% of the global energy consumption making it equal to the demand of aviation industry \cite{koomey}. Moreover, in many current data centers the actual IT equipment uses only half of the total energy whereas most of the remaining part is required for cooling and air movement resulting in poor Power Usage Effectiveness (PUE) \cite{pue} values. Large energy needs and significant $CO_2$ emissions caused that issues related to cooling, heat transfer, and IT infrastructure location are more and more carefully studied during planning and operation of data centers.
+Rising popularity of large-scale computing infrastructures caused quick development of data centers. Nowadays, data centers are responsible for around 2\% of the global energy consumption making it equal to the demand of aviation industry \cite{koomey}. Moreover, in many current data centers the actual IT equipment uses only half of the total energy whereas most of the remaining part is required for cooling and air movement resulting in poor Power Usage Effectiveness (PUE) \cite{pue} values. Large energy needs of data centers led to increased interest in cooling, heat transfer, and IT infrastructure location during planning and operation of data centers.
 
 For these reasons many efforts were undertaken to measure and study energy efficiency of data centers. There are projects focused on data center monitoring and management \cite{games}\cite{fit4green} whereas others on  energy efficiency of networks \cite{networks} or distributed computing infrastructures, like grids \cite{fit4green_carbon_scheduler}. Additionally, vendors offer a wide spectrum of energy efficient solutions for computing and cooling \cite{sgi}\cite{colt}\cite{ecocooling}. However, a variety of solutions and configuration options can be applied planning new or upgrading existing data centers.
@@ -177 +177 @@
 To demonstrate DCworms capabilities we evaluate impact of several resource management policies on overall energy-efficiency of specific workloads executed on heterogeneous resources.
 
-The remaining part of this paper is organized as follows. In Section~2 we give a brief overview of the current state of the art concerning modeling and simulation of distributed systems, such as Grids and Clouds, in terms of energy efficiency. Section~3 discusses the main features of DCworms. In particular, it introduces our approach to workload and resource management, presents the concept of energy efficiency modeling and explains how to incorporate a specific application performance model into simulations. Section~4 discusses energy models adopted within the DCworms. In Section~5 we assess the energy models by comparison of simulation results with real measurements. We also present experiments that were performed using DCworms to show various types of resource and scheduling technics allowing decreasing the total energy consumption of the execution of a set of tasks. In Section~6 we explain how to integrate workload and resource simulations with heat transfer simulations within the CoolEmAll project. Final conclusions and directions for future work are given in Section~7.
+The remaining part of this paper is organized as follows. In Section~2 we give a brief overview of the current state of the art concerning modeling and simulation of distributed systems, such as Grids and Clouds, in terms of energy efficiency. Section~3 discusses the main features of DCworms. In particular, it introduces our approach to workload and resource management, presents the concept of energy efficiency modeling and explains how to incorporate a specific application performance model into simulations. Section~4 discusses energy models adopted within the DCworms. In Section~5 we assess the energy models by comparison of simulation results with real measurements. We also present experiments that were performed using DCworms to show various types of resource and scheduling technics allowing decreasing the total energy consumption of the execution of a set of tasks. Final conclusions and directions for future work are given in Section~6.
 
 \section{Related Work}\label{sota}
@@ -322 +322 @@
 
 \begin{equation}
-P=C\cdot V_{core}^{2}\cdot f\label{eq:ohm-law}
-\end{equation}
-
-with $C$ being the processor switching capacitance, $V_{core}$ the
-current P-State's core voltage and $f$ the frequency. Based on the
+P=C\cdot V^{2}\cdot f\label{eq:ohm-law}
+\end{equation}
+
+with $C$ being the processor switching capacitance, $V$ the
+current P-State's voltage and $f$ the frequency. Based on the
 above equation it is suggested that although the reduction of frequency
 causes an increase in the time of execution, the reduction of frequency
-also leads to the reduction of $V_{core}$ and thus the power savings
-from the $P\sim V_{core}^{2}$ relation outweigh the increased computation
-time. However, experiments performed on several HPC servers show that this dependency does not reflect theoretical shape and is often close to linear as presented in Figure \ref{fig:power_freq}. This phenomenon can be explained by impact of other component than CPU and narrow range of available voltages. A good example of impact by other components is power usage of servers with visible influence of fans as illustrated in Figure \ref{fig:fans_P}.
+also leads to the reduction of $V$ and thus the power savings
+from the $P\sim V^{2}$ relation outweigh the increased computation
+time. However, experiments performed on several HPC servers show that this dependency does not reflect theoretical shape and is often close to linear as presented in Figure \ref{fig:power_freq} for Actina Solar 212 server equipped with a 4 core Intel Xeon 5160 of “Woodcrest” family having four P-States (2.0, 2.3, 2.6 and 3.0GHz). This phenomenon can be explained by impact of other component than CPU and narrow range of available voltages. A good example of impact by other components is power usage of servers with visible influence of fans as illustrated in Figure \ref{fig:fans_P}.
 
 For these reasons, DCworms allows users to define dependencies between power usage and resource states (such as CPU frequency) in the form of tables or arbitrary functions using energy estimation plugins.