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Pattern analysis based data management method and memory-disk integrated system for high performance computing

机译：基于模式分析的高性能计算数据管理方法和存储磁盘集成系统

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摘要

This research proposes a memory-disk integrated system (MDIS) with pattern analysis based data management method. The proposed system consists of a pattern adaptive prefetcher with DRAM-based dual buffers and a PCM-based persistent memory structure. The PCM-based persistent memory module is exploited as conventional main memory and storage layers simultaneously. In this system, to compensate the PCMs unfavorable characteristics, a pattern analysis based prefetching method by using dual DRAM buffers with a small amount of space are designed as a sort of caching layer. The proposed pattern analysis based data management method monitors the miss rate to determine the prefetch policy, e.g., when and what to prefetch. These schemes enable flexible adaptation to the application execution characteristics showing unpredictable and inconsistent workload patterns. In order to evaluate our system, we implemented a trace-driven simulator system and launched the YCSB on Redis, Apache Storm, Apache Spark, and OpenStack Swift. The experimental result shows that the MDIS with a pattern adaptive prefetcher can reduce the total access time by 13.9% compared to the conventional scheme and thus the overall performance of the memory-disk integrated system improves.
机译：本研究提出了一种基于模式分析的数据管理方法的存储磁盘集成系统（MDIS）。拟议的系统由一个模式自适应预取器组成，该预取器具有基于DRAM的双缓冲区和基于PCM的持久性存储结构。基于PCM的持久性存储模块同时被用作常规的主存储器和存储层。在该系统中，为了补偿PCM的不利特性，通过使用具有少量空间的双DRAM缓冲器将基于模式分析的预取方法设计为一种缓存层。所提出的基于模式分析的数据管理方法监视未命中率以确定预取策略，例如，何时以及什么预取。这些方案使得能够灵活地适应应用程序执行特性，从而显示出不可预测的和不一致的工作负载模式。为了评估我们的系统，我们实施了跟踪驱动的模拟器系统，并在Redis，Apache Storm，Apache Spark和OpenStack Swift上启动了YCSB。实验结果表明，与传统方案相比，带有模式自适应预取器的MDIS可以将总访问时间减少13.9％，从而提高了存储磁盘集成系统的整体性能。

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