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12/25/12 14:33:34 (12 years ago)
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ariel
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abstract i czesc intro

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

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  • papers/SMPaT-2012_DCWoRMS/elsarticle-DCWoRMS.tex

    r699 r712  
    116116%% Text of abstract 
    117117 
     118In the recent years, the issue of computing infrastructures energy-efficiency has gained great attention. In this paper we present a Data Center Workload and Resource Management Simulator (DCWoRMS) which enables modeling and simulations of computing infrastructures to estimate their performance, energy consumption, and energy-efficiency metrics for diverse workloads and management policies. 
     119We discuss methods of power usage modeling available in the simulator. To this end, we compare results of simulations to measurements from the real servers.  
     120To demonstrate DCWoRMS capabilities we evaluate impact of several resource management policies on overall energy-efficiency of specific workloads on heterogeneous resources. 
     121 
    118122\end{abstract} 
    119123 
    … …  
    136140\section{Introduction} 
    137141 
    138 TODO - Introduction 
    139  
    140 ... 
     142Data 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. 
     143Even 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.  
     144Furthermore, available power supply is usually limited so it also may reduce data center development capabilities, especially looking at challenges related to exascale computing breakthrough foreseen within this decade. 
     145 
     146For these reasons many efforts were undertaken to measure and study energy efficiency of data centers. Some of projects focused on data center monitoring and management \cite{games}\cite{fit4green} whereas others on prototypes of low power computing infrastructures \cite{montblanc}. Studies included aspects such as energy efficiency of networks \cite{networks} and service level agreements related to energy consumption \cite{sla}. Additionally, vendors offer a wide spectrum of energy efficient solutions for computing and cooling \cite{sgi}\cite{colt}\cite{ecocooling}. However, a variety of solutions and configuration options can be applied planning new or upgrading existing data centers. 
     147In order to optimize a design or configuration of data center we need a thorough study using appropriate metrics and tools evaluating how much computation or data processing  can be done within given power and energy budget and how it affects temperatures, heat transfers, and airflows within data center.  
     148Therefore, there is a need for simulation tools and models that approach the problem from a perspective of end users and take into account all the factors that are critical to understanding and improving the energy efficiency of data centers, in particular, hardware characteristics, applications, management policies, and cooling. 
     149These tools should support data center designers and operators by answering questions how specific application types, levels of load, hardware specifications, physical arrangements, cooling technology, etc. impact overall data center energy efficiency.  
     150 
     151In this paper we present a Data Center Workload and Resource Management Simulator (DCWoRMS) which enables modeling and simulations of computing infrastructures to estimate their performance, energy consumption, and energy-efficiency metrics for diverse workloads and management policies. 
     152We discuss methods of power usage modeling available in the simulator. To this end, we compare results of simulations to measurements from the real servers.  
     153To demonstrate DCWoRMS capabilities we evaluate impact of several resource management policies on overall energy-efficiency of specific workloads on heterogeneous resources. 
     154 
     155TODO - update 
    141156 
    142157The 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. 
    … …  
    507522% \bibitem{} 
    508523 
     524\bibitem[15]{fit4green} [15] 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. 
     525 
    509526\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. 
    510527 
    … …  
    513530\bibitem{DCD_Romonet} http://www.datacenterdynamics.com/blogs/ian-bitterlin/it-does-more-it-says-tin\%E2\%80\%A6 
    514531 
     532\bibitem[16]{networks} [16] E. Gelenbe and C. Morfopoulou. Power savings in packet networks via optimised routing. Mobile Networks and Applications, 17(1):152–159, February 2012. 
     533 
    515534\bibitem{Ghislain} Ghislain Landry Tsafack Chetsa, Laurent LefÚvre, Jean-Marc Pierson, Patricia Stolf, Georges Da Costa. “DNA-inspired Scheme for Building the Energy Profile of HPC Systems”. In: International Workshop on Energy-Efficient Data Centres, Madrid, Springer, 2012 
    516535 
     536\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. Iványi, (Editors), Civil-Comp Press, Stirlingshire, Scotland 
     537 
    517538\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 
    518539 
     540\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. 
     541 
    519542\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. 
    520543 
    521544\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. 
    522545 
     546\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 
     547 
     548\bibitem[8]{koomey} 
     549[8] Koomey, Jonathan. 2008. "Worldwide electricity used in data centers." Environmental Research Letters. vol. 3, no. 034008. September 23 
     550 
     551 
     552 
     553% web links 
     554 
    523555\bibitem{GWF} http://gwa.ewi.tudelft.nl/ 
    524556 
    … …  
    530562 
    531563 
     564\bibitem[19]{colt} [19] Colt Modular Data Centre, http://www.colt.net/uk/en/products-services/data-centre-services/modular-data-centre-en.htm 
     565 
     566\bibitem[20]{coolemall} [20] The CoolEmAll project website, http://coolemall.eu  
     567 
     568\bibitem[21]{ecocooling} [21] EcoCooling, http://www.ecocooling.org 
     569 
     570\bibitem[22]{montblanc} [22] The MontBlanc project website,  http://www.montblanc-project.eu/ 
     571 
     572\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 
     573 
     574\bibitem[24]{sgi} [24] SGI ICE Cube Air, http://www.sgi.com/products/data\_center/ice\_cube\_air/ 
     575 
     576 
    532577 \end{thebibliography} 
    533578 
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