Index: /papers/SMPaT-2012_DCWoRMS/elsarticle-DCWoRMS.fdb_latexmk
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--- /papers/SMPaT-2012_DCWoRMS/elsarticle-DCWoRMS.fdb_latexmk	(revision 960)
+++ /papers/SMPaT-2012_DCWoRMS/elsarticle-DCWoRMS.fdb_latexmk	(revision 1045)
@@ -1,4 +1,4 @@
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Index: /papers/SMPaT-2012_DCWoRMS/elsarticle-DCWoRMS.tex
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--- /papers/SMPaT-2012_DCWoRMS/elsarticle-DCWoRMS.tex	(revision 959)
+++ /papers/SMPaT-2012_DCWoRMS/elsarticle-DCWoRMS.tex	(revision 1045)
@@ -192,5 +192,5 @@
 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 virtualization-based approaches. CloudSim allows creating a simple resources hierarchy consisting of machines and processors. To simulate a real cloud computing data center, it provides an extra virtualization layer responsible for the VM provisioning process and managing the VM life cycle. In DCSG Simulator user is able to take into account a variety of mechanical and electrical devices as well as the IT equipment and define for each of them numerous factors, including device capacity and efficiency as well as the data center conditions.
+The scope of the aforementioned toolkits concerns the data center environments. However, all of them, except DCWoRMS presented in this paper, restricts the simulated  architecture in terms of types of modeled resources. In this way, they impose the use of predefined sets of resources and relations between them. 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 virtualization-based approaches. CloudSim allows creating a simple resources hierarchy consisting of machines and processors. To simulate a real cloud computing data center, it provides an extra virtualization layer responsible for the virtual machines (VM) provisioning process and managing the VM life cycle. In DCSG Simulator user is able to take into account a variety of mechanical and electrical devices as well as the IT equipment and define for each of them numerous factors, including device capacity and efficiency as well as the 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  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.
@@ -227,5 +227,5 @@
 \subsection{Workload modeling}
 
-As it was said, experiments performed in DCWoRMS require a description of applications that will be scheduled during the simulation. As a primary definition, DCWoRMS uses files in the Standard Workload Format (SWF) or its extension the Grid Workload Format (GWF) \cite{GWF}. In addition to the SWF file, some more detailed specification of a job and tasks can be included in an auxiliary XML file. This form of description provides the scheduler with more detailed information about application profile, task requirements, user preferences and execution time constraints, which are unavailable in SWF/GWF files. To facilitate the process of adapting the traces from real resource management systems, DCWoRMS supports reading those delivered from the most common ones like SLURM \cite{SLURM} and Torque \cite{TORQUE}. 
+As it was said, experiments performed in DCWoRMS require a description of applications that will be scheduled during the simulation. As a primary definition, DCWoRMS uses files in the Standard Workload Format (SWF) or its extension the Grid Workload Format (GWF) \cite{GWF}. In addition to the SWF file, some more detailed specification of a job and tasks can be included in an auxiliary XML file. This form of description extends the basic one and provides the scheduler with more detailed information about application profile, task requirements, user preferences and execution time constraints, which are unavailable in SWF/GWF files. To facilitate the process of adapting the traces from real resource management systems, DCWoRMS supports reading those delivered from the most common ones like SLURM \cite{SLURM} and Torque \cite{TORQUE}. 
 Since the applications may vary depending on their nature in terms of their requirements and structure, DCWoRMS provides user flexibility in defining the application model. Thus, considered workloads may have various shapes and levels of complexity that range from multiple independent jobs, through large-scale parallel applications, up to whole workflows containing time dependencies and preceding constraints between jobs and tasks. Each job may consist of one or more tasks and these can be seen as groups of processes. Moreover, DCWoRMS is able to handle rigid and moldable jobs, as well as pre-emptive ones. To model the application profile in more detail, 
 DCWoRMS follows the DNA approach proposed in \cite{Ghislain}. Accordingly, each task can be presented as a sequence of phases, which shows the impact of this task on the resources that run it. Phases are then periods of time where the system is stable (load, network, memory) given a certain threshold. Each phase is linked to values of the system that represent a resource consumption profile. Such a stage could be for example described as follows: â60\% CPU, 30\% net, 10\% mem.â
@@ -242,5 +242,5 @@
 This form of representation allows users to define a wide range of workloads:
 HPC (long jobs, computational-intensive, hard to migrate) or virtualization (short requests) typical for cloud computing environments.
-Further, the DCWoRMS benefits from the GSSIM workload generator tool and extends it with that allows creating synthetic workloads.
+Further, the DCWoRMS benefits from the GSSIM workload generator tool that allows creating synthetic workloads.
 
 
@@ -588,6 +588,6 @@
 
 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 consists 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 used the DCWoRMS workload generator tool using the following characteristics (Table~\ref{workloadCharacteristics}).
+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 four workloads varying in load intensity (10\%, 30\%, 50\%, 70\%). The differences in the load were obtained by applying various intervals (3000, 1200, 720 and 520 seconds, respectively) related to submission times of two successive tasks. In all cases the number of tasks was equal to 1000. Moreover, we differentiated the applications in terms of number of cores allocated by them and their type.
+To generate a workload we used the DCWoRMS workload generator tool with the aforementioned characteristics gathered in Table~\ref{workloadCharacteristics}.
 
 \begin {table}[ tp]
@@ -749,5 +749,5 @@
 \end {table}
 
-Referring to the Table~\ref{loadEnergy}, one should easily note that gain from switching off unused nodes decreases with the increasing workload density. In general, for the highly loaded system such policy does not find an application due to the cost related to this process and relatively small benefits. Another interesting conclusion, refers to the poor result for Random strategy with downgrading the frequency approach. The lack of improvement on the energy usage criterion for higher system load can be explained by the relatively small or no benefit obtained for prolonging the task execution, and thus, maintaining the node in working state. The cost of longer workload completion, can not be compensate by the very little energy savings derived from the lower operating state of node.
+Referring to the Table~\ref{loadEnergy}, one should easily note that gain from switching off unused nodes decreases with the increasing workload density. In general, for the highly loaded system such policy does not find an application due to the cost related to this process and relatively small benefits. Another interesting conclusion, refers to the poor result for Random strategy with downgrading the frequency approach. The lack of improvement on the energy usage criterion for higher system load can be explained by the relatively small or no benefit obtained for prolonging the task execution, and thus, maintaining the node in working state. The cost of longer workload completion, can not be compensate by the very little energy savings derived from the lower operating state of node. The greater criteria values for the higher system load are the result of greater time space between submission of successive tasks, and thus longer workload execution.
 We also demonstrated differences between power usage models. They span from rough static approach to accurate application specific models. However, the latter can be difficult or even infeasible to use as it requires real measurements for specific applications beforehand. This issue can be partially resolved by introducing application profiles and classification, which can deteriorate the accuracy though. This issue is begin studied more deeply within CoolEmAll project. 
 
Index: /papers/SMPaT-2012_DCWoRMS/elsarticle-DCWoRMS.aux
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--- /papers/SMPaT-2012_DCWoRMS/elsarticle-DCWoRMS.aux	(revision 959)
+++ /papers/SMPaT-2012_DCWoRMS/elsarticle-DCWoRMS.aux	(revision 1045)
@@ -84,11 +84,11 @@
 \@writefile{lot}{\contentsline {table}{\numberline {5}{\ignorespaces  Workload characteristics}}{20}}
 \newlabel{workloadCharacteristics}{{5}{20}}
-\@writefile{toc}{\contentsline {subsection}{\numberline {5.5}Computational analysis}{20}}
+\@writefile{toc}{\contentsline {subsection}{\numberline {5.5}Computational analysis}{21}}
 \@writefile{toc}{\contentsline {subsubsection}{\numberline {5.5.1}Random approach}{21}}
-\@writefile{toc}{\contentsline {subsubsection}{\numberline {5.5.2}Energy optimization}{21}}
 \@writefile{lof}{\contentsline {figure}{\numberline {6}{\ignorespaces  Random strategy}}{22}}
 \newlabel{fig:70r}{{6}{22}}
 \@writefile{lof}{\contentsline {figure}{\numberline {7}{\ignorespaces  Random + switching off unused nodes strategy}}{22}}
 \newlabel{fig:70rnpm}{{7}{22}}
+\@writefile{toc}{\contentsline {subsubsection}{\numberline {5.5.2}Energy optimization}{22}}
 \@writefile{lot}{\contentsline {table}{\numberline {6}{\ignorespaces  Measured power of testbed nodes in idle state}}{23}}
 \newlabel{idlePower}{{6}{23}}
@@ -107,5 +107,5 @@
 \@writefile{lot}{\contentsline {table}{\numberline {8}{\ignorespaces  Makespan [s] for different level of system load. R - Random, R+NPM - Random + node power management, EO - Energy optimization, EO+NPM - Energy optimization + node power management, R+LF - Random + lowest frequency}}{27}}
 \newlabel{loadMakespan}{{8}{27}}
-\@writefile{toc}{\contentsline {section}{\numberline {6}Conclusions and future work}{27}}
+\@writefile{toc}{\contentsline {section}{\numberline {6}Conclusions and future work}{28}}
 \bibcite{fit4green}{{1}{}{{}}{{}}}
 \bibcite{e2dc12}{{2}{}{{}}{{}}}
