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Conference Paper

dCache, scalable managed storage

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GES_paper77.pdf
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Citation

Fuhrmann, P., & Mkrtchyan, T. (2007). dCache, scalable managed storage.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-B136-6
Abstract
End of 2007, the most challenging high energy physics experiment ever, the Large Hadron Collider(LHC)[9], at CERN, will start to produce a sustained stream of data in the order of 300MB/sec, equivalent to a stack of CDs as high as the Eiffel Tower once per week. This data is, while produced, distributed and persistently stored at several dozens of sites around the world, building the LHC data grid. The destination sites are expected to provide the necessary middle-ware, so called Storage Elements, offering standard protocols to receive the data and optionally store it at the site specific Tertiary Storage Systems. Beside its actual functionality, discussed subsequently, the Storage Element software has to be able to fit into a large variety of environments. They are known to range from sites providing a single storage box of some Tera Bytes of data and nearly no maintenance personnel up to Tier I sites with estimated disk storage capacities reaching into the Peta Byte area. Moreover, sites expected to store data permanently may want to use their already existing Hierarchical Storage Management (HSM) System to drive the robotics. This requires the Storage Element to be aware of HSM Systems and to be able to manage external file copies. The wide range of scalability, from the very small to the limits of affordable storage, is one of the primary goals of dCache, the Storage Element introduced in this presentation. By being strictly compliant to standard data transfer and control protocols, like gsiFtp, xRootd and the Storage Resource Manager protocol SRM, we are focusing on our second goal which is to make dCache available and useful beyond the borders of the High Energy Physics Community. Beside storing and preparing data for transfer, dCache provides a rich palette of functions to manage the available storage, as will be described subsequently. This includes replication of datasets on automated detection of busy storage components as well as optimization of access to tertiary storage systems.