Abstract
In this paper the data life cycle management is extended by accounting for energy consumption during the life cycle of files. Information about the energy consumption of data not only allows to account for the correct costs of its life cycle, but also provides a feedback to the user and administrator, and improves awareness of the energy consumption of file I/O. Ideas to realize a storage landscape which determines the energy consumption for maintaining and accessing each file are discussed. We propose to add new extended attributes to file metadata which enable to compute the energy consumed during the life cycle of each file.
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References
Astrophysical Research Consortium (2010) The sloan digital sky survey. http://www.sdss.org/
CERN (2008) The large hadron collider. http://public.web.cern.ch/public/en/LHC/LHC-en.html
Colarelli D, Grunwald D (2002) Massive arrays of idle disks for storage archives. In: Supercomputing ’02: proceedings of the 2002 ACM/IEEE conference on supercomputing. IEEE Computer Society, Los Alamitos, pp 1–11
Deelman E, Chervenak A (2008) Data management challenges of data-intensive scientific workflows. In: CCGRID ’08: proceedings of the eighth IEEE international symposium on cluster computing and the grid. IEEE Computer Society, Los Alamitos, pp 687–692
Gil Y, Deelman E, Ellisman M, Fahringer T, Fox G, Gannon D, Goble C, Livny M, Moreau L, Myers J (2007) Examining the challenges of scientific workflows. Computer 40(12):24–32
Greenawalt P (1994) Modeling power management for hard disks. In: The conference on modeling, analysis, and simulation of computer and telecommunication systems, pp. 62–66
Hazelhurst S (2008) Scientific computing using virtual high-performance computing: a case study using the Amazon elastic computing cloud. In: SAICSIT ’08: proceedings of the 2008 annual research conference of the South African institute of computer scientists and information technologists on IT research in developing countries. ACM, New York, pp 94–103
Kuntz SK, Murphy RC, Niemier MT, Izaguirre JA, Kogge PM (2001) Petaflop computing for protein folding. In: Proceedings of the tenth SIAM conference on parallel processing for scientific computing
Molaro D, Payer H, Le Moal D (2009) Tempo: disk drive power consumption characterization and modeling. In: Consumer electronics. IEEE Computer Society, Los Alamitos
Nijim M, Manzanares A, Ruan X, Qin X (2009) HYBUD: an energy-efficient architecture for hybrid parallel disk systems. In: ICCCN ’09: proceedings of the 18th international conference on computer communications and networks. IEEE Computer Society, Los Alamitos, pp 1–6
Oracle (2010) StorageTek SL8500—power calculator. http://www.sun.com/calc/storage/tape_storage/tape_libraries/sl8500/index.html
Scibilia F (2007) Accounting of storage resources in glite based infrastructures. In: WETICE ’07: proceedings of the 16th IEEE international workshops on enabling technologies: infrastructure for collaborative enterprises. IEEE Computer Society, Los Alamitos, pp 273–278
Steinke T (2000) Tools for parallel quantum chemistry software. In: Modern methods and algorithms of quantum chemistry. NIC series, vol 1. John von Neumann Institute for Computing, Jülich, pp 49–67
Valle M (2004) Scientific data management. http://www.cscs.ch/mvalle/sdm/scientific-data-management.html
Vengerov D (2008) A reinforcement learning framework for online data migration in hierarchical storage systems. J Supercomput 43(1):1–19
Zedlewski J, Sobti S, Garg N, Zheng F, Krishnamurthy A, Wang R, Wang O (2003) Modeling hard-disk power consumption
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Kunkel, J.M., Mordvinova, O., Kuhn, M. et al. Collecting energy consumption of scientific data. Comput Sci Res Dev 25, 197–205 (2010). https://doi.org/10.1007/s00450-010-0121-5
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DOI: https://doi.org/10.1007/s00450-010-0121-5