In this paper we propose an approach for executing data transformations near- or in-storage on intelligent storage systems. The currently prevailing approach of extracting the data and then transforming it to a target format suffers degraded performance during transformation and causes heavy data movement. Our results show robust performance of foreground workloads and lower resource contention. Our vision draws architectural opportunities in multi-engine and multi-system settings, as well as for reuse.

翻译：本文提出一种在智能存储系统上近存储或存内执行数据转换的方法。当前主流的数据提取后再转换至目标格式的方法，在转换过程中存在性能下降问题，并引发大量数据迁移。我们的实验结果表明，该方法能保证前台工作负载的稳健性能并降低资源争用。我们的构想为多引擎与多系统架构场景提供了新的设计机遇，并具备良好的可复用性。