Index: papers/SMPaT-2012_DCWoRMS/elsarticle-DCWoRMS.tex
===================================================================
--- papers/SMPaT-2012_DCWoRMS/elsarticle-DCWoRMS.tex	(revision 712)
+++ papers/SMPaT-2012_DCWoRMS/elsarticle-DCWoRMS.tex	(revision 713)
@@ -46,4 +46,5 @@
 %% The amssymb package provides various useful mathematical symbols
 \usepackage{amssymb}
+\usepackage{multirow}
 %% The amsthm package provides extended theorem environments
 %% \usepackage{amsthm}
@@ -159,23 +160,20 @@
 \section{Related Work}
 
-TODO - shorten, correct (ITS A DRAFT VERSION)
-
-The growing importance of energy efficiency in information technologies led to significant interest in energy saving methods for computing systems. Therefore, intelligent resource management policies are gaining popularity when considering the energy efficiency of IT infrastructures. Nevertheless, studies of impact of scheduling strategies on energy consumption require a large effort and are difficult to perform in real distributed environments. To overcome these issues extensive research has been conducted in the area of modeling and simulation tools. As a result, a wide variety of simulation tools that address the green computing issues in distributed infrastructures have been proposed. Among them the most popular ones are: GreenCloud \cite{GreenCloud}, CloudSim \cite{CloudSim} and DCSG Simulator \cite{DCSG}
-
-GreenCloud is a C++ based simulation environment for studying the energy-efficiency of cloud computing data centers CloudSim is a simulation tool that allows modeling of cloud computing environments and evaluation of resource provisioning algorithms. Finally, the DCSG Simulator is a data center cost and energy simulator calculating the power and cooling schema of the data center equipment. 
-
-Although the aforementioned toolkits are capable of establishing the data center environment, none of them provides user with flexibility in the term of detailed resource modeling. GreenCloud defines switches, links and servers that are representedas single core nodes that are responsible for task execution and may contain different scheduling strategies. Contrary to what the GreenCloud name may suggest, it does not allow testing the impact of a virtualization-based approach on the resource management. 
-CloudSim allows creating a simple resources hierarchy containing computing and network resources that consist of machines and processors. To simulate a real cloud computing data center, it provides an extra virtualization layer that acts as an execution, management, and hosting environment for application services. It is responsible for the VM provisioning process as well as managing the VM life cycle with respect to the cost metrics and economic policies.
-In DCSG Simulator, user is able to take into account a wide variety of mechanical and electrical devices and for each of them numerous factors can be defined, including device capacity and efficiency as well as data center conditions. Apart from data center facilities the IT equipment can be modeled and simulated.
-
-The general idea behind all of the analyzed tools is to enable studies concerning energy efficiency in distributed infrastructures. GreenCloud approach enables fine-grained modeling of energy usage associated with computing servers and network components. For example, the server power consumption model implemented in GreenCloud depends on the server state as well as its utilization. The CloudSim framework provides basic models to validate and evaluate energy-conscious provisioning of techniques and algorithms. Each computing node can be extended with a power model that estimates the current the power consumption. Within the DCSG Simulator, performance of each data center equipment (facility and IT) is determined by a combination of factors, including workload, local conditions, the manufacturer's specifications of the machine's components and the way in which the machine is utilized based on its provisioned IT load. In DCWoRMS, the plugin idea has been introduced that offers emulating the behavior of computing resources in terms of power consumption. Additionally, it delivers detailed information concerning resource and application characteristics needed to define more sophisticated power draw models.
-
-In order to emulate the behavior of real computing systems, green computing simulator should address also the energy-aware resource management. In this term, GreenCloud offers capturing the effects of both of the Dynamic Voltage and Frequency Scaling (DVFS) and Dynamic Power Management (DPM) schemes. At the links and switches level, it supports downgrading the transmission rate and putting some network equipment into a sleep mode, respectively. CloudSim comes with a set of predefined and extendable policies that manage the process of VM migrations according to the total energy consumed in order to optimize the power consumption. However, the proposed approach is not sufficient for modeling more sophisticated policies like frequency scaling techniques and managing resource power states. With respect to the white paper, Romonetâs tool implements a set of basic energy-efficient rules that have been developed on the basis of detailed understanding of the data center as a system. The output of this simulation is a set of energy and cost data representing the IT devices (including PUE and DCiE). DCWoRMS introduces the dedicated interface that provides methods to determine detailed information about each resource and its components energy state and allows changing its current energy state. As this interface is accessible from scheduling plugins, energy management may be admitted as a part of whole scheduling process. Availability of these interfaces in schedulers supports implement different strategies such as centralized energy management, self-management of computing resources and mixed models.
-
-In terms of application modeling, all tools, except DCSG Simulator, describe the application with a number of computational and communicational requirements. In addition, GreenCloud and DCWoRmS allow introducing the Qos requirements (typical for cloud computing applications) by taking into account the time constraints during the simulation. DCSG Simulator instead of modeling of the single application, introduce the definition of workload applied the computing devices that leads to a given utilization level. Nevertheless, only DCWoRMS supports application performance modeling. In the case of other tools, aforementioned capabilities allow only incorporating simple requirements that are taken into account during scheduling, they do not affect the application execution. On the other hand CloudSim offers modeling of utilization models that are used to estimate the current load of processor, bandwidth and memory and can be taken into account during the task allocation process. 
-
-GreenCloud, CloudSim and DCWoRMS are released as Open Source under the General Public License Agreement. Romonetâs tool is available under an OSL V3.0 open-source license, however, it can be only accessed by the DCSG members.
-
-
+The growing importance of energy efficiency in information technologies led to significant interest in energy saving methods for computing systems. Nevertheless, studies of impact of resource management policies on energy efficiency of IT infrastructures require a large effort and are difficult to perform in real distributed environments. To overcome these issues extensive research has been conducted in the area of modeling and simulation and variety of tools that address the green computing issues in distributed infrastructures has been proposed. Among them the most popular ones are: GreenCloud \cite{GreenCloud}, CloudSim \cite{CloudSim} and DCSG Simulator \cite{DCSG}.
+
+GreenCloud is a C++ based simulation environment for studying the energy-efficiency of cloud computing data centers. CloudSim is a simulation tool that allows modeling of cloud computing environments and evaluation of resource provisioning algorithms. Finally, the DCSG Simulator is a data center cost and energy simulator calculating the power and cooling schema of the data center equipment. 
+
+The scope of the aforementioned toolkits concerns the data center environments. However, all of them, except DCWoRMS presented in this paper, imposes and restricts user in terms of modeled resources. GreenCloud defines switches, links and servers that are responsible for task execution and may contain different scheduling strategies. Contrary to what the GreenCloud name may suggest, it does not allow testing the impact of a virtualization-based approach on the resource management. CloudSim allows creating a simple resources hierarchy that consist of machines and processors. To simulate a real cloud computing data center, it provides an extra virtualization layer that is responsible for the VM provisioning process as well as managing the VM life cycle. In DCSG Simulator, user is able to take into account a wide variety of mechanical and electrical devices as well as the IT equipment and for each of them numerous factors can be defined, including device capacity and efficiency as well as data center conditions.
+
+The general idea behind all of the analyzed tools is to enable studies concerning energy efficiency in distributed infrastructures. GreenCloud approach enables simulation of energy usage associated with computing servers and network components. For example, the server power consumption model implemented in GreenCloud depends on the server state as well as its utilization. The CloudSim framework provides basic models to evaluate energy-conscious provisioning policies. Each computing node can be extended with a power model that estimates the current the power consumption. Within the DCSG Simulator, performance of each data center equipment (facility and IT) is determined by a combination of factors, including workload, local conditions, the manufacturer's specifications and the way in which it is utilized. In DCWoRMS, the plugin idea has been introduced that offers emulating the behavior of computing resources in terms of power consumption. Additionally, it delivers detailed information concerning resource and application characteristics needed to define more sophisticated power draw models.
+
+In order to emulate the behavior of real computing systems, green computing simulator should address also the energy-aware resource management. In this term, GreenCloud offers capturing the effects of both of the Dynamic Voltage and Frequency Scaling (DVFS) and Dynamic Power Management schemes. At the links and switches level, it supports downgrading the transmission rate and putting some network equipment into a sleep mode. CloudSim comes with a set of predefined and extensible policies that manage the process of VM migrations according to the total energy consumed in order to optimize the power consumption. However, the proposed approach is not sufficient for modeling more sophisticated policies like frequency scaling techniques and managing resource power states. Romonetâs tool is told to implement a set of basic energy-efficient rules that have been developed on the basis of detailed understanding of the data center as a system. The output of this simulation is a set of energy, like PUE, and cost data representing the IT devices. DCWoRMS introduces a dedicated interface that provides methods to obtain the detailed information about each resource and its components energy consumption and allows changing its current energy state. Availability of these interfaces in scheduling plugin supports implementation of various strategies such as centralized energy management, self-management of computing resources and mixed models.
+
+In terms of application modeling, all tools, except DCSG Simulator, describe the application with a number of computational and communicational requirements. In addition, GreenCloud and DCWoRMS allow introducing the QoS requirements (typical for cloud computing applications) by taking into account the time constraints during the simulation. DCSG Simulator instead of modeling of the single application, enables the definition of workload that leads to a given utilization level. However, only DCWoRMS supports application performance modeling by not only incorporating simple requirements that are taken into account during execution, but also by allowing specification of task execution time.
+
+GreenCloud, CloudSim and DCWoRMS are released as Open Source under the GPL. Romonetâs tool is available under an OSL V3.0 open-source license, however, it can be only accessed by the DCSG members.
+
+Summarizing, DCWoRMS stand out from other tools due to the flexibility in terms of data center equipment and structure definition.
+Moreover, it allows to associate the energy consumption not only with the current power state and resource utilization but also with the particular set of applications running on it. The main strength of CloudSim lies in implementation of the complex scheduling and task execution schemes involving resource virtualization techniques. However,the energy efficiency aspect is limited only to the VM management, The GreenCloud focuses on data center resources with particular attention to the network infrastructure and the most popular energy management approaches. DCSG simulator allows to take into account also non-computing devices, nevertheless it seems to be hardly customizable tool.
 
 \section{DCWoRMS}
@@ -419,15 +417,10 @@
 \section{Experiments and evaluation}\label{sec:experiments}
 
-Results + RECS and MOP description
-
-....
-
 In this section, we present computational analysis that were conducted to emphasize the role of modelling and simulation in studying computing systems performance. We carried out two types of experiments. The former one aimed at demonstrating the capabilities of the simulator in termis of verifying the research hypotheses. The latter set of experiments was performed on the CoolEmAll testbed and then repeated using DCWoRMS tool. The comparative analysis of obtained results shows the reproducibility of experiments and prove the correctness of the adopted models and assumptions.
 
 \subsection{Testbed description}
 
-The RECS Cluster System is an 18 node computer system that has an monitoring and controlling mechanism integrated. Through the integrated novel monitoring approach of the RECS Cluster System the network load can be reduced, the dependency of polling every single
-compute node at operation system layer can be avoided. Furthermore this concept build up a basis on which new monitoring- and controlling-concepts can be developed. Therefore, each compute node of the RECS Cluster Server is connected to an Operation System independent microcontroller that collects the most important sensor data like temperature, power consumption and the status (on/off) from every single node.
-
+
+To obtain values of power consumption that could be later used in DCWoRMS environment to build the model and to evaluate resource management policies we ran a set of applications / benchmarks on the physical testbed. For experimental purposes we choose the high-density Resource Efficient Cluster Server (RECS) system. The single RECS unit consists of 18 single CPU modules, each of them can be treated as an individual node of PC class. Configuration of our RECS unit is presented in Table~\ref{testBed}. The RECS system was chosen due to its heterogeneous platform with very high density and energy efficiency that has a monitoring and controlling mechanism integrated. The built-in and additional sensors allow to monitor the complete testbed at a very fine granularity level without the negative impact of the computing- and network-resources. 
 
 \begin {table}[ tp]
@@ -438,8 +431,7 @@
 Type & Memory (RAM) & Count  \\
 \hline
- Intel i7 & 16 GB &	4 \\
+ Intel i7 & 16 GB &	8 \\
 AMD Fusion T40N 64 Bit & 4 GB 	& 6 \\
 Atom D510 64 Bit & 2 GB &	4 \\
-Atom Z510 VT &2 GB &	4 \\
 \hline
 \multicolumn{3}{c}{Storage} \\
@@ -449,19 +441,133 @@
 \hline
 \end{tabular}
-\caption {CoolEmAll testbed}
+\caption {\label{testBed} CoolEmAll testbed}
 \end {table}
 
-%Node i7, 16 GB RAM	4
-%Node AMD Fusion T40N Dualcore, 1,0 Ghz, 4 GB (64 Bit)	6
-%Node Atom D510 64 Bit, 2 GB	4
-%Node Atom Z510 VT, 2 GB	4
-%RECS | Storage Head 520, 16 x 300 GB SSD, 2 x 10 Gbit/s CX4
-
-\subsection{Computational analysis}
-
-TODO - experiments
+\subsection{Evaluated applications}
 
 To demonstrate capabilities of the simulator in terms of energy efficiency modeling we present examples of experiments performed using the DCWoRMS. First we carried out a set of tests on the real hardware used as a CoolEmAll testbed to build the performance and energy profile of applications. Then we applied this data into the simulation environment and used to investigate different approaches to energy-aware resource management. 
 The following applications were evaluated:
+
+\textbf{Abinit} is a widely-used application for computational physics simulating systems made of electrons and nuclei to be calculated within density functional theory.
+
+\textbf{C-Ray} is a ray-tracing benchmark that stresses floating point performance of a CPU. The test is configured with the 'scene' file at 16000x9000 resolution.
+
+\textbf{Linpack} benchmark is used to evaluate system floating point performance. It is based on the Gaussian elimination methods that solves a dense N by N system of linear equations.
+
+\textbf{Tar} it is a widely used data archiving software [tar]. In the tests the task was to create one compressed file of Linux kernel, which is about 2,3GB size, using bzip2.
+
+\textbf{FFTE} benchmark measures the floating-point arithmetic rate of double precision complex one-dimensional Discrete Fourier Transforms of 1-, 2-, and 3-dimensional sequences of length $2^{p} * 3^{q} * 5^{r}$. In our tests only one core was used to run the application
+
+
+
+\subsection{Methodology}
+
+Every chosen application / benchmark was executed on each type of node, for all frequencies supported by the CPU and for different levels of parallelization (number of cores).  To eliminate the problem with assessing which part of the power consumption comes from which application, in case when more then one application is ran on the node, the queuing system (SLURM) was configured to run jobs in exclusive mode (one job per node). Such configuration is often used for at least dedicated part of HPC resources. The advantage of the exclusive mode scheduling policy consist in that the job gets all the resources of the assigned nodes for optimal parallel performance and applications running on the same node do not influence each other. For every configuration of application, type of node and CPU frequency we measure the average power consumption of the node and the execution time. The aforementioned values  were used to configure the DCWoRMS environment providing energy and time execution models.
+Based on the models obtained for the considered set of resources and applications we evaluated a set of resource management strategies in terms of energy consumption needed to execute three workloads varying in load intensity (10\%, 30\%, 50\%, 70\% ).
+To generate a workload we benefited from the DCWoRMS workload generator tool using the following characteristics.
+
+\begin {table}[ tp]
+
+\begin{tabular}{lllllr}
+\hline
+Characteristic & \multicolumn{4}{c}{Load intensity} & Distribution\\
+& 10  & 30 & 50 & 70  \\
+\hline
+Task Count & \multicolumn{4}{c}{1000} & constant\\
+\hline
+Task Interval [s] & 3000 & 1200 & 720 & 520 & poisson\\
+\hline
+\multirow{8}{*}{Number of cores to run}  & \multicolumn{4}{c}{1} & uniform- 30\%\\
+ & \multicolumn{4}{c}{2} & uniform - 30\%\\
+ & \multicolumn{4}{c}{3} & uniform - 10\%\\
+ & \multicolumn{4}{c}{4} & uniform - 10\%\\
+ & \multicolumn{4}{c}{5} & uniform - 5\%\\
+ & \multicolumn{4}{c}{6} & uniform - 5\%\\
+ & \multicolumn{4}{c}{7} & uniform - 5\%\\
+ & \multicolumn{4}{c}{8} & uniform - 5\%\\
+\hline
+\multirow{5}{*}{Application type}  & \multicolumn{4}{c}{Abinit} & uniform- 20\%\\
+ & \multicolumn{4}{c}{C-Ray} & uniform - 20\%\\
+ & \multicolumn{4}{c}{Tar} & uniform - 20\%\\
+ & \multicolumn{4}{c}{Linpack} & uniform - 20\%\\
+ & \multicolumn{4}{c}{FFT} & uniform - 20\%\\
+
+\hline
+\end{tabular}
+\caption {\label{workloadChatacteristics} Workload characteristics}
+\end {table}
+
+Execution time of each application is based on the measurements collected within our testbed.
+In all cases we assumed that  tasks are scheduled and served in order of their arrival (FIFO strategy with easy backfilling strategy). 
+
+\subsection{Computational analysis}
+
+TODO - correct, improve, refactor...
+
+The following section discusses the results obtained for the workload with load density equal to 70\% in the light of five resource management and scheduling strategies. 
+The first considered by us policy was the strategy in which tasks were assigned to nodes in random manner with the reservation that they can be assigned only to nodes of the type which the application was possible to execute on and we have the corresponding value of power consumption and execution time. The random strategy is only the reference one and will be later used to compare benefits in terms of energy efficiency resulting from more sophisticated algorithms. Two versions of the strategy were considered. The former one in which unused nodes are not switched off, which case is still the the primary one in many HPC centers and the former one getting more popular due to energy costs in which unused nodes are switched off to reduce the total energy consumption.
+
+
+\begin{figure}[h!]
+\centering
+\includegraphics[width = 12cm]{fig/70r.png}
+\caption{\label{fig:70r} Random strategy}
+\end{figure}
+
+\textbf{total energy usage [kWh]} : 46,883
+\textbf{mean power consumption [W]} : 316,17
+\textbf{workload completion [s]} : 266 347
+
+\begin{figure}[h!]
+\centering
+\includegraphics[width = 6cm]{fig/70rnpm.png}
+\caption{\label{fig:70rnpm} Random + switching off unused nodes strategy}
+\end{figure}
+
+\textbf{total energy usage [kWh]} : 36,705
+\textbf{mean power consumption [W]} : 247,53
+\textbf{workload completion [s]} : 266 347
+
+The next two evaluate resource management strategies try to decrease the total energy consumption needed to execute the whole workload taking into account differences in applications and hardware profiles.  We tried to match both profiles to find the more energy efficient assignment. In the first case we assumed that there is again no possibility to switch off unused nodes, thus for the whole time needed to execute workload nodes consume at least power for idle state. To obtain the minimal energy consumption, tasks has to be assigned to the nodes of type for which the difference between energy consumption for the node running the application and in the idle state is minimal.
+
+
+\begin{figure}[h!]
+\centering
+\includegraphics[width = 12cm]{fig/70eo.png}
+\caption{\label{fig:70eo} Energy usage optimization strategy}
+\end{figure}
+
+\textbf{total energy usage [kWh]} : 46,305
+\textbf{mean power consumption [W]} : 311,94
+\textbf{workload completion [s]} : 265 822
+
+
+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.
+
+
+
+\begin{figure}[h!]
+\centering
+\includegraphics[width = 12cm]{fig/70eonpm.png}
+\caption{\label{fig:70eonpm} Energy usage optimization + switching off unused nodes strategy}
+\end{figure}
+
+\textbf{total energy usage [kWh]} : 30,568
+\textbf{mean power consumption [W]} : 206,15
+\textbf{workload completion [s]} : 264 944
+
+The last considered by us case is modification of the one of previous strategies taking into account the energy-efficiency of nodes. We assume that tasks do not have deadlines and the only criterion which is taken into consideration is the total energy consumption. All the considered workloads have been executed on the testbed configured for three different possible frequencies of CPUs â the lowest, medium and the highest one. The experiment was intended to check if the benefit of running the workload on less power-consuming frequency of CPU is not leveled by the prolonged time of execution of the workload.
+
+
+\begin{figure}[h!]
+\centering
+\includegraphics[width = 12cm]{fig/70dfs.png}
+\caption{\label{fig:70dfs} Frequency downgrading strategy}
+\end{figure}
+
+\textbf{total energy usage [kWh]} : 77,108
+\textbf{mean power consumption [W]} : 260,57
+\textbf{workload completion [s]} : 445 886
+
 
 ....
@@ -522,5 +628,5 @@
 % \bibitem{}
 
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+\bibitem{fit4green} A. Berl, E. Gelenbe, M. di Girolamo, G. Giuliani, H. de Meer, M.-Q. Dang, K. Pentikousis. Energy-Efficient Cloud Computing. The Computer Journal, 53(7), 2010.
 
 \bibitem{CloudSim} Rodrigo N. Calheiros, Rajiv Ranjan, Anton Beloglazov, Cesar A. F. De Rose, and Rajkumar Buyya, CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms, Software: Practice and Experience (SPE), Volume 41, Number 1, Pages: 23-50, ISSN: 0038-0644, Wiley Press, New York, USA, January, 2011.
@@ -530,13 +636,13 @@
 \bibitem{DCD_Romonet} http://www.datacenterdynamics.com/blogs/ian-bitterlin/it-does-more-it-says-tin\%E2\%80\%A6
 
-\bibitem[16]{networks} [16] E. Gelenbe and C. Morfopoulou. Power savings in packet networks via optimised routing. Mobile Networks and Applications, 17(1):152â159, February 2012.
+\bibitem{networks} E. Gelenbe and C. Morfopoulou. Power savings in packet networks via optimised routing. Mobile Networks and Applications, 17(1):152â159, February 2012.
 
 \bibitem{Ghislain} Ghislain Landry Tsafack Chetsa, Laurent LefÃšvre, Jean-Marc Pierson, Patricia Stolf, Georges Da Costa. âDNA-inspired Scheme for Building the Energy Profile of HPC Systemsâ. In: International Workshop on Energy-Efficient Data Centres, Madrid, Springer, 2012
 
-\bibitem[6]{games} [6] A. Kipp, L. Schubert, J. Liu, T. Jiang, W. Christmann, M. vor dem Berge (2011). Energy Consumption Optimisation in HPC Service Centres, Proceedings of the Second International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering, B.H.V. Topping and P. IvaÌnyi, (Editors), Civil-Comp Press, Stirlingshire, Scotland
+\bibitem{games} A. Kipp, L. Schubert, J. Liu, T. Jiang, W. Christmann, M. vor dem Berge (2011). Energy Consumption Optimisation in HPC Service Centres, Proceedings of the Second International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering, B.H.V. Topping and P. IvaÌnyi, (Editors), Civil-Comp Press, Stirlingshire, Scotland
 
 \bibitem{GreenCloud} D. Kliazovich, P. Bouvry, and S. U. Khan, A Packet-level Simulator of Energy- aware Cloud Computing Data Centers, Journal of Supercomputing, vol. 62, no. 3, pp. 1263-1283, 2012
 
-\bibitem[17]{sla} [17] S. Klingert, T. Schulze, C. Bunse. GreenSLAs for the Energy-efficient Management of Data Centres. 2nd International Conference on Energy-Efficient Computing and Networking (e-Energy), 2011.
+\bibitem{sla} S. Klingert, T. Schulze, C. Bunse. GreenSLAs for the Energy-efficient Management of Data Centres. 2nd International Conference on Energy-Efficient Computing and Networking (e-Energy), 2011.
 
 \bibitem{GSSIM} S. Bak, M. Krystek, K. Kurowski, A. Oleksiak, W. Piatek and J. Weglarz, GSSIM - a Tool for Distributed Computing Experiments, Scientific Programming Journal, vol. 19, no. 4, pp. 231-251, 2011.
@@ -544,8 +650,8 @@
 \bibitem{GSSIM_Energy} M. Krystek, K. Kurowski, A. Oleksiak, W. Piatek, Energy-aware simulations with GSSIM. Proceedings of the COST Action IC0804 on Energy Efficiency in Large Scale Distributed Systems, 2010, pp. 55-58.
 
-\bibitem[2]{hintemann} [2] Hintemann, R., Fichter, K. (2010). Materialbestand der Rechenzentren in Deutschland, Eine Bestandsaufnahme zur Ermittlung von Ressourcen- und Energieeinsatz, UBA, Texte, 55/2010
-
-\bibitem[8]{koomey}
-[8] Koomey, Jonathan. 2008. "Worldwide electricity used in data centers." Environmental Research Letters. vol. 3, no. 034008. September 23
+\bibitem{hintemann} Hintemann, R., Fichter, K. (2010). Materialbestand der Rechenzentren in Deutschland, Eine Bestandsaufnahme zur Ermittlung von Ressourcen- und Energieeinsatz, UBA, Texte, 55/2010
+
+\bibitem{koomey}
+Koomey, Jonathan. 2008. "Worldwide electricity used in data centers." Environmental Research Letters. vol. 3, no. 034008. September 23
 
 
@@ -562,15 +668,15 @@
 
 
-\bibitem[19]{colt} [19] Colt Modular Data Centre, http://www.colt.net/uk/en/products-services/data-centre-services/modular-data-centre-en.htm
-
-\bibitem[20]{coolemall} [20] The CoolEmAll project website, http://coolemall.eu 
-
-\bibitem[21]{ecocooling} [21] EcoCooling, http://www.ecocooling.org
-
-\bibitem[22]{montblanc} [22] The MontBlanc project website,  http://www.montblanc-project.eu/
-
-\bibitem[23]{pue} [23] The Green Grid Data Center Power Efficiency Metrics: PUE and DCiE, http://www.thegreengrid.org/Global/Content/white-papers/The-Green-Grid-Data-Center-Power-Efficiency-Metrics-PUE-and-DCiE
-
-\bibitem[24]{sgi} [24] SGI ICE Cube Air, http://www.sgi.com/products/data\_center/ice\_cube\_air/
+\bibitem{colt} Colt Modular Data Centre, http://www.colt.net/uk/en/products-services/data-centre-services/modular-data-centre-en.htm
+
+\bibitem{coolemall} The CoolEmAll project website, http://coolemall.eu 
+
+\bibitem{ecocooling} EcoCooling, http://www.ecocooling.org
+
+\bibitem{montblanc} The MontBlanc project website,  http://www.montblanc-project.eu/
+
+\bibitem{pue} The Green Grid Data Center Power Efficiency Metrics: PUE and DCiE, http://www.thegreengrid.org/Global/Content/white-papers/The-Green-Grid-Data-Center-Power-Efficiency-Metrics-PUE-and-DCiE
+
+\bibitem{sgi} SGI ICE Cube Air, http://www.sgi.com/products/data\_center/ice\_cube\_air/
 
 
