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Index: /papers/SMPaT-2012_DCWoRMS/elsarticle-DCWoRMS.tex
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 \section{Introduction}
 
+TODO - update 
+
 Data centers are responsible for around 2\% of the global energy consumption making it equal to the demand of aviation industry \cite{koomey}. In many current data centers the actual IT equipment uses only half of the total energy (e.g. 45-62\% in \cite{hintemann}) while 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.
 Even if we take ecological and footprint issues aside, the amount of consumed energy can impose strict limits on data centers. First of all, energy bills may reach millions euros making computations expensive. 
@@ -154,26 +156,24 @@
 To demonstrate DCWoRMS capabilities we evaluate impact of several resource management policies on overall energy-efficiency of specific workloads on heterogeneous resources.
 
-TODO - update
-
 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 various 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}
 
-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}.
+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 have emerged. 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 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 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 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.
+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 network equipment into a sleep mode. CloudSim comes with a set of predefined and extensible policies that manage the process of VM migrations 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 scheduling, 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.
+Summarizing, DCWoRMS stands 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. Moreover, it does not limit the user in defining various types of resource management polices. 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}
