VAT: Asymptotic Cost Analysis for Multi-Level Key-Value Stores
Nikos Batsaras, Giorgos Saloustros, Anastasios Papagiannis, Panagiota, Fatourou, and Angelos Bilas

TL;DR
This paper introduces VAT, an asymptotic cost analysis method for multi-level key-value stores, enabling comparison of design tradeoffs and guiding future development, especially for fast storage devices like SSDs and NVM.
Contribution
VAT provides a novel analytical framework to evaluate insert-path amplification and performance tradeoffs in multi-level KV stores, surpassing limited experimental analyses.
Findings
Randomness in insert-path reduces amplification by over 10x on fast storage.
Key-value separation and tiering reduce amplification by 10x and 5x respectively.
Advancements in NVM technology diminish the benefits of certain optimization techniques.
Abstract
Over the past years, there has been an increasing number of key-value (KV) store designs, each optimizing for a different set of requirements. Furthermore, with the advancements of storage technology the design space of KV stores has become even more complex. More recent KV-store designs target fast storage devices, such as SSDs and NVM. Most of these designs aim to reduce amplification during data reorganization by taking advantage of device characteristics. However, until today most analysis of KV-store designs is experimental and limited to specific design points. This makes it difficult to compare tradeoffs across different designs, find optimal configurations and guide future KV-store design. In this paper, we introduce the Variable Amplification- Throughput analysis (VAT) to calculate insert-path amplification and its impact on multi-level KV-store performance.We use VAT to…
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Taxonomy
TopicsAdvanced Data Storage Technologies · Cloud Computing and Resource Management · Image and Video Quality Assessment
