6533b831fe1ef96bd1299037
RESEARCH PRODUCT
Random Slicing: Efficient and Scalable Data Placement for Large-Scale Storage Systems
Toni CortesAlberto MirandaYangwook KangIvan PopovTom FriedetzkyEthan L. MillerSascha EffertAndré Brinkmannsubject
DesignComputer scienceDistributed computingPerformancestorage managementHash function0102 computer and information sciences02 engineering and technologyParallel computingUSable01 natural sciencesSlicingrandomized data distributionAffordable and Clean Energy0202 electrical engineering electronic engineering information engineeringRandomnessExperimentationscalabilityPseudorandom number generatorbusiness.industry020206 networking & telecommunicationsReliabilityData FormatPRNG010201 computation theory & mathematicsHardware and ArchitectureComputer data storageScalabilityTable (database)businessNetworking & Telecommunicationsdescription
The 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 article 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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2014-07-01 |