HotDataTrap: a sampling-based hot data identification scheme for flash memory

  • Authors:

  • Dongchul Park;Biplob Debnath;Youngjin Nam;David H. C. Du;Youngkyun Kim;Youngchul Kim

  • Affiliations:

  • University of Minnesota, Twin Cities;University of Minnesota, Twin Cities;University of Minnesota, Twin Cities;University of Minnesota, Twin Cities;Electronics and Telecommunications Research Institute (ETRI), Korea;Electronics and Telecommunications Research Institute (ETRI), Korea

  • Venue:
  • Proceedings of the 27th Annual ACM Symposium on Applied Computing
  • Year:
  • 2012

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Abstract

Hot data identification is an issue of paramount importance in flash-based storage devices since it has a great impact on their overall performance as well as retains a big potential to be applicable to many other fields. However, it has been least investigated. HotDataTrap is a novel on-line hot data identification scheme adopting a sampling mechanism. This sampling-based algorithm enables HotDataTrap to early discard some of the cold items so that it can reduce runtime overheads and a waste of memory spaces. Moreover, its two-level hierarchical hash indexing scheme helps HotDataTrap directly look up a requested item in the cache and save a memory space further by exploiting spatial localities. Both our sampling approach and hierarchical hash indexing scheme empower HotDataTrap to precisely and efficiently identify hot data with a very limited memory space. Our extensive experiments with various realistic workloads demonstrate that our HotDataTrap outperforms the state-of-the-art scheme by an average of 335% and and our two-level hash indexing scheme considerably improves further HotDataTrap performance up to 50.8%.