Reliable and Randomized Data Distribution Strategies for Large Scale Storage Systems

Appeared in Proceedings of HiPC 2011.

Abstract

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 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, helping it to drastically reduce the required amount of randomness while delivering a perfect load distribution.

Publication date:
December 2011

Authors:
Alberto Miranda
Sascha Effert
Yangwook Kang
Ethan L. Miller
Andre Brinkmann
Toni Cortes

Projects:
Ultra-Large Scale Storage

Available media

Full paper text: PDF

Bibtex entry

@inproceedings{miranda-hipc11,
  author       = {Alberto Miranda and Sascha Effert and Yangwook Kang and Ethan L. Miller and Andre Brinkmann and Toni Cortes},
  title        = {Reliable and Randomized Data Distribution Strategies for Large Scale Storage Systems},
  booktitle    = {Proceedings of HiPC 2011},
  month        = dec,
  year         = {2011},
}