2014/01/24 by Niv Dayan, Dayan, Niv, Martin Kjær Svendsen +9
Computer Science · #Advanced Data Storage Technologies #Algorithms and Data Compression #Caching and Content Delivery #Databases (cs.DB) #FOS: Computer and information sciences #cs.DB
paper · pdf · doi:10.48550/arxiv.1401.6360
39th International Conference on Very Large Data Bases (VLDB) (2013)
arxiv created 2014/01/24 · openalex publication_date 2014/01/24 · arxiv updated 2014/01/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Solid State Drives (SSDs) are a moving target for system designers: they are black boxes, their internals are undocumented, and their performance characteristics vary across models. There is no appropriate analytical model and experimenting with commercial SSDs is cumbersome, as it requires a careful experimental methodology to ensure repeatability. Worse, performance results obtained on a given SSD cannot be generalized. Overall, it is impossible to explore how a given algorithm, say a hash join or LSM-tree insertions, leverages the intrinsic parallelism of a modern SSD, or how a slight change in the internals of an SSD would impact its overall performance. In this paper, we propose a new SSD simulation framework, named EagleTree, which addresses these problems, and enables a principled study of SSD-Based algorithms. The demonstration scenario illustrates the design space for algorithms based on an SSD-based IO stack, and shows how researchers and practitioners can use EagleTree to perform tractable explorations of this complex design space.