Index: papers/SMPaT-2012_DCWoRMS/elsarticle-DCWoRMS.tex
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--- papers/SMPaT-2012_DCWoRMS/elsarticle-DCWoRMS.tex	(revision 651)
+++ papers/SMPaT-2012_DCWoRMS/elsarticle-DCWoRMS.tex	(revision 657)
@@ -140,5 +140,5 @@
 ...
 
-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, like 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 present some experiments that were performed on our testbed and then repeated using DCWORMS to evaluate the correctness of the simulation environment. Section~6 focuses on the role of DCWoRMS 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, like 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 present some experiments that were performed using DCWoRMS utilizing real testbed nodes models to show varius types of popular resource and scheduling technics allowing to decrease the total power consumption of the execution of a set of tasks. Section~6 focuses on the role of DCWoRMS within the CoolEmAll project. Final conclusions and directions for future work are given in Section~7.
 
 \section{Related Work}
@@ -151,11 +151,11 @@
 
 To deliver information about the energy usage, GreenCloud distinguishes three energy consumption components: computing energy, communicational energy, and the energy component related to the physical infrastructure of a data center. This approach enables modeling energy usage associated with computations, network operations and cooling systems. In GreenCloud, the energy models are implemented for every simulated data center entity (computing servers, core and rack switches). Moreover, due to the advantage in the simulation resolution, energy models can operate at the network packet level as well. This allows updating the levels of energy consumption whenever a new packet leaves or arrives from the link, or whenever a new task execution is started or completed at the server.
-Servers are modeled as singe core nodes that are responsible for task execution and may contain different scheduling strategies.
-The server power consumption model implemented in GreenCloud depends on the server state and its utilization and allows 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, GreenCloud supports Dynamic Voltage Scaling (DVS) and Dynamic Network Shutdown (DNS) techniques. The DVS method introduced a control element at each port of the switch that - depending on the traffic pattern and current levels of link utilization - could downgrade the transmission rate. DNS approach allows putting some network equipment into sleep mode. 
+Servers are modeled as single core nodes that are responsible for task execution and may contain different scheduling strategies.
+The server power consumption model implemented in GreenCloud depends on the server state as well as its utilization and allows 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, GreenCloud supports Dynamic Voltage Scaling (DVS) and Dynamic Network Shutdown (DNS) techniques. The DVS method introduces a control element at each port of the switch that - depending on the traffic pattern and current levels of link utilization - could downgrade the transmission rate. The DNS approach allows putting some network equipment into a sleep mode. 
 
 To cover the vast majority of cloud computing applications, GreenCloud defines three types of workloads: computationally intensive workloads that load computing servers considerably, data-intensive workloads that require heavy data transfers, and finally balanced workloads which aim to model the applications having both computing and data transfer requirements.
 GreenCloud describes application with a number of computational requirements. Moreover, it specifies communication requirements of the applications in terms of the amount of data to be transferred before and after a task completion. The execution of each application requires a successful completion of its two main components: computing and communicational. 
-In addition time constraints can be taken into account during the simulation by adding a predefined execution deadline, which aims at introducing Quality of Service constraints specified in a Service Level Agreement. Nevertheless, GreenCloud does not support application performance modeling. Aforementioned capabilities allow only incorporating simple requirements that need to be satisfied before and during task execution.
+In addition time constraints can be taken into account during the simulation by adding a predefined execution deadline, which aims at introducing Quality of Service constraints specified in a Service Level Agreement. Nevertheless, GreenCloud does not support application performance modeling. Aforementioned capabilities allow only incorporating simple requirements that need to be satisfied before and during the task execution.
 
 Contrary to what the GreenCloud name may suggest, it does not allow testing the impact of a virtualization-based approach on the resource management. 
@@ -167,12 +167,12 @@
 CloudSim \cite{CloudSim} is an event-based simulation tool written in Java. Initially CloudSim was based on the well-known GridSim framework, however since the last few releases it is an independent simulator and does not benefit from most of the GridSim functionality. 
 
-CloudSim allows creating a simple resource hierarchy containing computing resources that consist of machines and processors. Additionally, it may simulate the behavior of other components including storage and network resources. However, it focuses on computational resources and 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 such as: VM creation, VM destruction, and VM migration. It also enables evaluation of different economic policies by modeling the cost metrics related to the SaaS and IaaS models.
-
-The CloudSim framework provides basic models and entities to validate and evaluate energy-conscious provisioning of techniques and algorithms. Each computing node can be extended with a power model that simulates the power consumption. CloudSim offers example implementations of this component that characterize some popular server models. Needless to say, it can be easily extended for simulating user-defined power consumption models. That allows estimating the current power usage according to the current utilization level or the host model. This capability enables the creation of energy-conscious provisioning policies that require real-time knowledge of power consumption by Cloud system components.
+CloudSim allows creating a simple resources hierarchy containing computing resources that consist of machines and processors. Additionally, it may simulate the behavior of other components including storage and network resources. However, it focuses on computational resources and 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 such as: VM creation, VM destruction, and VM migration. It also enables evaluation of different economic policies by modeling the cost metrics related to the SaaS and IaaS models.
+
+The CloudSim framework provides basic models and entities to validate and evaluate energy-conscious provisioning of techniques and algorithms. Each computing node can be extended with a power model that simulates the power consumption. CloudSim offers example implementations of this component that characterize some popular server models. Needless to say, it can be easily extended for simulating user-defined power consumption models. That allows estimating the current power usage according to the utilization level or the host model. This capability enables the creation of energy-conscious provisioning policies that require real-time knowledge of power consumption by Cloud system components.
 Furthermore, it allows an accounting of the total energy consumed by the system during the simulation period. CloudSim comes with a set of predefined and extendable policies that manage the process of VM migrations in order to optimize the power consumption. However, the proposed solution is not appropriate for more sophisticated power management policies. In particular, CloudSim is not sufficient for modeling frequency scaling techniques and managing resource power states.
 
 Similar to GreenCloud, CloudSim defines a simple application model that includes computational and data requirements. Although all these constraints are taken into account during scheduling, they do not affect the application execution. Thereby, a researcher is required to put a lot of effort to incorporate an application performance model into his experiments.
 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.
-Concerning workloads, simulator is able to partially support SWF \cite{SWF} files and read data in a user-defined file format. Moreover, it can handle a wide variety of workload types, including parallel, and pre-emptive jobs
+Concerning workloads, simulator is able to partially support SWF \cite{SWF} files and read data in a user-defined file format. Moreover, it can handle a wide variety of workload types, including parallel, and pre-emptive jobs.
 
 CloudSim is available as Open Source under GPL license.
@@ -181,11 +181,11 @@
 \subsection{DCSG Simulator}
 
-DCSG Simulator \cite{DCSG} is a Data Centre Cost and Energy Simulator that has been developed under the Carbon Trust Low Carbon Collaborations program in conjunction with the BCS and Romonet Ltd. The simulator works at both a data center infrastructure level where analysis of the achieved efficiency of the data center mechanical and electrical plant can be performed but also at the IT level. The simulator implements a set of basic rules that have been developed, based on a detailed understanding of the data center as a system, to allow cost and energy use to be usefully allocated to IT devices within the data center. 
-
-As far as data center infrastructure level is concerned, DCSG Simulator is calculates the power and cooling schema of data center equipment with respect to their performance. User is able to take into account a wide variety of mechanical and electrical devices like: transformers, power distribution units, power supply, cabling, computer room air conditioning units and chiller plant. For each of them a numerous factors can be defined, including device capacity and efficiency, load operating points. These data can be derived from a generic list as well as from the information given by particular manufacturers. There is a wide range of pre-defined models, but user can easily extend them or create a new ones.
+DCSG Simulator \cite{DCSG} is a Data Centre Cost and Energy Simulator that has been developed under the Carbon Trust Low Carbon Collaborations program in conjunction with the BCS and Romonet Ltd. The simulator works at a data center infrastructure level where analysis of the achieved efficiency of the data center mechanical and electrical plant can be performed but also at the IT level. The simulator implements a set of basic rules that have been developed, based on a detailed understanding of the data center as a system, to allow cost and energy use to be usefully allocated to IT devices within the data center. 
+
+As far as data center infrastructure level is concerned, DCSG Simulator calculates the power and cooling schema of data center equipment with respect to their performance. User is able to take into account a wide variety of mechanical and electrical devices like: transformers, power distribution units, power supply, cabling, computer room air conditioning units and chiller plant. For each of them numerous factors can be defined, including device capacity and efficiency, load operating points. These data can be derived from a generic list as well as from the information given by particular manufacturers. There is a wide range of pre-defined models, but user can easily extend them or create new ones.
 
 To perform the IT simulation, it is possible to extend the data center infrastructure by putting IT devices into that data center. That enables detailed simulation of the energy efficiency of devices across a specified time period. 
 In this case performance of each piece of equipment (facility and IT) within a data center is determined by a combination of factors, including workload, data center 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. 
-Users are possible to bind the operational characteristics, proper to the particular geographic locations, with the simulation process. These characteristics may include temperature profile as well as the power cost that vary depending on the time and place. The output of this simulation is a set of energy and cost data representing the IT devices (including PUE and metrics) and data center energy consumption, capital and operational costs.
+Users are possible to bind the operational characteristics, proper to the particular geographic locations, with the simulation process. These characteristics may include temperature profile as well as the power cost that vary depending on the time and place. The output of this simulation is a set of energy and cost data representing the IT devices (including PUE and DCiE) and data center energy consumption, capital and operational costs.
 
 
@@ -204,5 +204,5 @@
 
 Data Center workload and resource management simulator (DCWoRMS) is a simulation tool based on the GSSIM framework \cite{GSSIM} developed by Poznan Supercomputing and Networking Center (PSNC).
-GSSIM has been proposed to provide an automated tool for experimental studies of various resource management and scheduling strategies in distributed computing systems. DCWoRMS extends its basic functionality and add some additional features related to the energy efficiency issues in data centers. In this section we will introduce the functionality of the simulator, in terms of modeling and simulation of large scale distributed systems like Grids and Clouds.
+GSSIM has been proposed to provide an automated tool for experimental studies of various resource management and scheduling strategies in distributed computing systems. DCWoRMS extends its basic functionality and adds some additional features related to the energy efficiency issues in data centers. In this section we will introduce the functionality of the simulator, in terms of modeling and simulation of large scale distributed systems like Grids and Clouds.
 
 
@@ -216,5 +216,5 @@
 \end{figure}
 
-DCWoRMS is an event-driven simulation tool written in Java. In general, input data for the DCWoRMS consist of workload and resources descriptions. They can be provided by the user, read from real traces or generated using the generator module. However, the key elements of the presented architecture are plugins. They allow the researchers to configure and adapt the simulation environment to the peculiarities of their studies, starting from modeling job performance, through energy estimations up to implementation of resource management and scheduling policies. Each plugin can be implemented independently and plugged into a specific experiment. Results of experiments are collected, aggregated, and visualized using the statistics module. Due to a modular and plug-able architecture DCWoRMS can be applied to specific resource management problems and addressing different usersâ requirements.
+DCWoRMS is an event-driven simulation tool written in Java. In general, input data for the DCWoRMS consist of workload and resources descriptions. They can be provided by the user, read from real traces or generated using the generator module. However, the key elements of the presented architecture are plugins. They allow the researchers to configure and adapt the simulation environment to the peculiarities of their studies, starting from modeling job performance, through energy estimations up to implementation of resource management and scheduling policies. Each plugin can be implemented independently and plugged into a specific experiment. Results of experiments are collected, aggregated, and visualized using the statistics module. Due to a modular and plug-able architecture DCWoRMS can be applied to specific resource management problems and address different usersâ requirements.
 
 
@@ -222,6 +222,6 @@
 
 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}. 
-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 a group 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 and. 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\%memâ
+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
 
 Levels of information about incoming jobs are presented in Figure~\ref{fig:jobsStructure}.
@@ -242,5 +242,5 @@
 \subsection{Resource modeling}
 
-The main goal of DCWoRMS is to enable researchers evaluation of various resource management policies in diverse computing environments. To this end, it supports flexible definition of simulated resources both on physical (computing resources) as well as on logical (scheduling entities) level. This flexible approach allows modeling various computing entities consisting of compute nodes, processors and cores. In addition, detailed location of the given resources can be provided in order to group them and arrange into physical structures such as racks and containers. Each of the components may be described by different parameters specifying available memory, storage capabilities, processor speed etc. In this way, it is possible to describe power distribution system and cooling devices. Due to an extensible description, users are able to define a number of experiment-specific and visionary characteristics. Moreover, with every component, dedicated profiles can be associated that determines, among others, power, thermal and air throughput properties. The energy estimation plugin can be bundled with each resource. This allows defining various power models that can be then followed by different computing system components. Details concerning the approach to energy-efficiency modeling in DCWoRMS can be found in the next sections.
+The main goal of DCWoRMS is to enable researchers evaluation of various resource management policies in diverse computing environments. To this end, it supports flexible definition of simulated resources both on physical (computing resources) as well as on logical (scheduling entities) level. This flexible approach allows modeling of various computing entities consisting of compute nodes, processors and cores. In addition, detailed location of the given resources can be provided in order to group them and arrange into physical structures such as racks and containers. Each of the components may be described by different parameters specifying available memory, storage capabilities, processor speed etc. In this way, it is possible to describe power distribution system and cooling devices. Due to an extensible description, users are able to define a number of experiment-specific and visionary characteristics. Moreover, with every component, dedicated profiles can be associated that determines, among others, power, thermal and air throughput properties. The energy estimation plugin can be bundled with each resource. This allows defining various power models that can be then followed by different computing system components. Details concerning the approach to energy-efficiency modeling in DCWoRMS can be found in the next sections.
 
 Scheduling entities allow providing data related to the brokering or queuing system characteristics. Thus, information about available queues, resources associated with them and their parameters like priority, availability of AR mechanism etc. can be defined. Moreover, allocation policy and task scheduling strategy for each scheduling entity can be introduced in form of the reference to an appropriate plugin. DCWoRMS allows building a hierarchy of schedulers corresponding to the hierarchy of resource components over which the task may be distributed.
@@ -251,5 +251,5 @@
 \subsection{Energy management concept in DCWoRMS}
 
-The DCWoRMS allows researchers to take into account energy efficiency and thermal issues in distributed computing experiments. That can be achieved by the means of appropriate models and profiles. In general, the main goal of the models is to emulate the behavior of the real computing resources, while profiles support models by providing data essential for the power consumption calulcations. Introducing particular models into the simulation environment is possible through choosing or implementation of dedicated energy plugins that contain methods to calculate power usage of resources, their temperature and system air throughput values. Presence of detailed resource usage information, current resource energy and thermal state description and a functional energy management interface enables an implementation of energy-aware scheduling algorithms. Resource energy consumption and thermal metrics become in this context an additional criterion in the resource management process. Scheduling plugins are provided with dedicated interfaces, which allow them to collect detailed information about computing resource components and to affect their behavior.
+The DCWoRMS allows researchers to take into account energy efficiency and thermal issues in distributed computing experiments. That can be achieved by the means of appropriate models and profiles. In general, the main goal of the models is to emulate the behavior of the real computing resources, while profiles support models by providing data essential for the power consumption calculations. Introducing particular models into the simulation environment is possible through choosing or implementation of dedicated energy plugins that contain methods to calculate power usage of resources, their temperature and system air throughput values. Presence of detailed resource usage information, current resource energy and thermal state description and a functional energy management interface enables an implementation of energy-aware scheduling algorithms. Resource energy consumption and thermal metrics become in this context an additional criterion in the resource management process. Scheduling plugins are provided with dedicated interfaces, which allow them to collect detailed information about computing resource components and to affect their behavior.
 The following subsections present the general idea behind the energy-efficiency simulations.
 
@@ -260,8 +260,8 @@
 
 \paragraph{\textbf{Power profile}}
-In general, power profiles allow specifying the power usage of resources. Depending on the accuracy of the model, users may provide additional information about power states which are supported by the resources, amounts of energy consumed in these states, and other information essential to calculate the total energy consumed by the resource during runtime. In such a way each component of IT infrastructure may be described, including computing resources, system components and data center facilities. Moreover, It is possible to define any number of new, resource specific, states, for example so called P-states, in which processor can operate.
+In general, power profiles allow specifying the power usage of resources. Depending on the accuracy of the model, users may provide additional information about power states which are supported by the resources, amounts of energy consumed in these states, and other information essential to calculate the total energy consumed by the resource during runtime. In such a way each component of IT infrastructure may be described, including computing resources, system components and data center facilities. Moreover, it is possible to define any number of new, resource specific, states, for example so called P-states, in which processor can operate.
 
 \paragraph{\textbf{Energy consumption model}}
-The main aim of these models is to emulate the behavior of the real computing resource and the way it consumes energy. Due to a rich functionality and flexible environment description, DCWoRMS can be used to verify a number of theoretical assumptions and develop new energy consumption models. Modeling of energy consumption is realized by the energy estimation plugin that calculates energy usage based on information about the resource power profile, resource utilization, and the application profile including energy consumption and heat production metrics. Relation between model and power profile is illustrated in Figure~\ref{fig:powerModel}.
+The main aim of these models is to emulate the behavior of the real computing resource and the way it consumes energy. Due to a rich functionality and flexible environment description, DCWoRMS can be used to verify a number of theoretical assumptions and to develop new energy consumption models. Modeling of energy consumption is realized by the energy estimation plugin that calculates energy usage based on information about the resource power profile, resource utilization, and the application profile including energy consumption and heat production metrics. Relation between model and power profile is illustrated in Figure~\ref{fig:powerModel}.
 
 \begin{figure}[tbp]
@@ -274,5 +274,5 @@
 DCWoRMS is complemented with an interface that allows scheduling plugins to collect detailed power information about computing resource components and to change their power states. It enables performing various operations on the given resources, including dynamically changing the frequency level of a single processor, turning off/on computing resources etc. The activities performed with this interface find a reflection in total amount of energy consumed by the resource during simulation. 
 
-Presence of detailed resource usage information, current resource energy state description and functional energy management interface enables an implementation of energy-aware scheduling algorithms. Resource energy consumption becomes in this context an additional criterion in the scheduling process, which use various techniques to decrease energy consumption, e.g. workload consolidation, moving tasks between resources to reach full load on one resource and zero load on the other or to balance the load, dynamic power management, cutting down CPU frequency, and others.
+Presence of detailed resource usage information, current resource energy state description and functional energy management interface enables an implementation of energy-aware scheduling algorithms. Resource energy consumption becomes in this context an additional criterion in the scheduling process, which use various techniques to decrease energy consumption, e.g. workload consolidation, moving tasks between resources to reduce a number of running resources, dynamic power management, cutting down CPU frequency, and others.
 
 \subsubsection{Air throughput management concept}
@@ -303,5 +303,5 @@
 
 \paragraph{\textbf{Thermal profile}}
-Thermal profile expresses the thermal specification of resources. It consists of the definition of the thermal design power (TDP), thermal resistance and thermal states that describe how the temperature depends on dissipated heat. For the purposes of more complex experiments, introducing of new, user-defined characteristics is supported. The aforementioned values may be provided for all computing system components distinguishing them, for instance, according to their material parameters and models.
+Thermal profile expresses the thermal specification of resources. It consists of the definition of the thermal design power (TDP), thermal resistance and thermal states that describe how the temperature depends on dissipated heat. For the purposes of more complex experiments, introducing of new, user-defined characteristics is supported. The aforementioned values may be provided for all computing system components distinguishing them, for instance, according to their material parameters and/or models.
 
 \paragraph{\textbf{Temperature estimation model}}
@@ -322,5 +322,5 @@
 \subsection{Application performance modeling}
 
-In general, DCWoRMS implement user application models as objects describing computational, communicational and energy requirements and profiles of the task to be scheduled. Additionally, simulator provides means to include complex and specific application performance models during simulations. They allow researchers to introduce specific ways of calculating task execution time. These models can be plugged into the simulation environment through a dedicated API and implementation of an appropriate plugin. To specify the execution time of a task user can apply a number of parameters, including:
+In general, DCWoRMS implements user application models as objects describing computational, communicational as well as energy requirements and profiles of the task to be scheduled. Additionally, simulator provides means to include complex and specific application performance models during simulations. They allow researchers to introduce specific ways of calculating task execution time. These models can be plugged into the simulation environment through a dedicated API and implementation of an appropriate plugin. To specify the execution time of a task user can apply a number of parameters, including:
 \begin{itemize}
   \item  task length (number of CPU instructions)
@@ -419,5 +419,5 @@
 ...
 
-Being based on the GSSIM framework, that has been successfully applied in a substantial number of research projects and academic studies, DCWoRMS with its sophisticated energy extension has become an essential tool for studies of energy efficiency in distributed environments. For this reason, it has been adopted within the CoolEmAll project as a component of SVD Toolkit. In general the main goal of CoolEmAll is to provide advanced simulation, visualisation and decision support tools along with blueprints of computing building blocks for modular data centre environments. Once developed, these tools and blueprints should help to minimise the energy consumption, and consequently the CO2 emissions of the whole IT infrastructure with related facilities. The SVD Toolkit is designed to support the analysis and optimization of IT modern infrastructures. For the recent years the special attention has been paid for energy utilized by the data centers which considerable contributes to the data center operational costs.  Actual power usage and effectiveness of energy saving methods heavily depends on available resources, types of applications and workload properties. Therefore, intelligent resource management policies are gaining popularity when considering the energy efficiency of IT infrastructures.
+Being based on the GSSIM framework, that has been successfully applied in a substantial number of research projects and academic studies, DCWoRMS with its sophisticated energy extension has become an essential tool for studies of energy efficiency in distributed environments. For this reason, it has been adopted within the CoolEmAll project as a component of Simulation, Visualisation and Decission Support (SVD) Toolkit. In general the main goal of CoolEmAll is to provide advanced simulation, visualisation and decision support tools along with blueprints of computing building blocks for modular data centre environments. Once developed, these tools and blueprints should help to minimise the energy consumption, and consequently the CO2 emissions of the whole IT infrastructure with related facilities. The SVD Toolkit is designed to support the analysis and optimization of IT modern infrastructures. For the recent years the special attention has been paid for energy utilized by the data centers which considerable contributes to the data center operational costs.  Actual power usage and effectiveness of energy saving methods heavily depends on available resources, types of applications and workload properties. Therefore, intelligent resource management policies are gaining popularity when considering the energy efficiency of IT infrastructures.
 Hence, SVD Toolkit integrates also workload management and scheduling policies to support complex modeling and optimization of modern data centres.
 
