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Resource abstraction and data placement for distributed hybrid memory pool

机译:分布式混合内存池的资源抽象和数据展示

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摘要

Emerging byte-addressable non-volatile memory (NVM) technologies offer higher density and lower cost than DRAM, at the expense of lower performance and limited write endurance. There have been many studies on hybrid NVM/DRAM memory management in a single physical server. However, it is still an open problem on how to manage hybrid memories efficiently in a distributed environment. This paper proposes Alloy, a memory resource abstraction and data placement strategy for an RDMA-enabled distributed hybrid memory pool (DHMP). Alloy provides simple APIs for applications to utilize DRAM or NVM resource in the DHMP, without being aware of the hardware details of the DHMP. We propose a hotness-aware data placement scheme, which combines hot data migration, data replication and write merging together to improve application performance and reduce the cost of DRAM. We evaluate Alloy with several micro-benchmark workloads and public benchmark workloads. Experimental results show that Alloy can significantly reduce the DRAM usage in the DHMP by up to 95%, while reducing the total memory access time by up to 57% compared with the state-of-the-art approaches.
机译:新兴字节可寻址的非易失性存储器(NVM)技术提供比DRAM更高的密度和更低的成本,以较低的性能和有限的写入耐久性。在单个物理服务器中有很多关于混合NVM / DRAM内存管理的研究。但是,关于如何在分布式环境中有效地管理混合存储器的情况下仍然是一个开放问题。本文提出了一种支持RDMA的分布式混合内存池(DHMP)的内存资源抽象和数据放置策略。合金为应用程序提供简单的API,以便在DHMP中使用DRAM或NVM资源,而不知道DHMP的硬件细节。我们提出了一种热情感知的数据放置方案,它将热数据迁移,数据复制和写入合并组合在一起,以提高应用程序性能并降低DRAM的成本。我们评估了几种微基准工作负载和公共基准工作负载的合金。实验结果表明,合金可以显着降低DHMP中的DRAM使用量高达95%,同时与最先进的方法相比将总内存访问时间降低至57%。

著录项

  • 来源
    《Frontiers of computer science》 |2021年第3期|153103.1-153103.11|共11页
  • 作者单位

    National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computing Science and Technology Huazhong University of Science and Technology Wuhan 430074 China;

    National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computing Science and Technology Huazhong University of Science and Technology Wuhan 430074 China;

    National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computing Science and Technology Huazhong University of Science and Technology Wuhan 430074 China;

    National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computing Science and Technology Huazhong University of Science and Technology Wuhan 430074 China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    load balance; distributed hybrid memory; clouds;

    机译:负载均衡;分布式混合记忆;云;

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