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181   $ctxt
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183   $acr
200 10$a2011 18th International Conference on High Performance Computing
210 31$a[Place of publication not identified]$cIEEE$d2011
215   $a1 online resource $cillustrations
300   $aBibliographic Level Mode of Issuance: Monograph
311 08$a9781457719516 
311 08$a1457719517 
330   $aThe ever-growing amount of data requires highly scalable storage solutions. The most flexible approach is to use storage pools that can be expanded and scaled down by adding or removing storage devices. To make this approach usable, it is necessary to provide a solution to locate data items in such a dynamic environment. This paper presents and evaluates the Random Slicing strategy, which incorporates lessons learned from table-based, rule-based, and pseudo-randomized hashing strategies and is able to provide a simple and efficient strategy that scales up to handle exascale data. Random Slicing keeps a small table with information about previous storage system insert and remove operations, drastically reducing the required amount of randomness while delivering a perfect load distribution.
606   $aHigh performance computing$vCongresses
615  0$aHigh performance computing
676   $a004.3
702   $aIEEE Staff
801  0$bPQKB
906   $aPROCEEDING
912   $a9910139114003321
996   $a2011 18th International Conference on High Performance Computing$92416972
997   $aUNINA