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引用次数: 0
摘要
我们提出的 HeterMM 是一个多功能框架,可在异构内存的 KV 存储中利用内存中的索引。HeterMM 采用插件编程模型,允许集成各种类型的索引。通过优先在 DRAM 中维护索引和热数据,HeterMM 最大限度地利用了 DRAM 的卓越性能。我们的评估表明,HeterMM 优于将 DRAM 中的索引转换为持久性索引的现有先进框架。此外,HeterMM 还能通过为特定场景精心选择合适的索引,超越特定于 NVM 的 KV 存储。
HeterMM: applying in-DRAM index to heterogeneous memory-based key-value stores
We propose HeterMM, a versatile framework that leverages in-DRAM indexes in KV stores on heterogeneous memory. HeterMM incorporates a plug-in programming model, allowing for the integration of various types of indexes. By prioritizing the maintenance of both index and hot data in DRAM, HeterMM maximizes the utilization of the superior performance of DRAM. Our evaluation demonstrates that HeterMM outperforms existing state-of-the-art frameworks that convert in-DRAM indexes to persistent ones. Furthermore, HeterMM can surpass NVM-specific KV stores by carefully selecting the appropriate index for specific scenarios.
期刊介绍:
Frontiers of Computer Science aims to provide a forum for the publication of peer-reviewed papers to promote rapid communication and exchange between computer scientists. The journal publishes research papers and review articles in a wide range of topics, including: architecture, software, artificial intelligence, theoretical computer science, networks and communication, information systems, multimedia and graphics, information security, interdisciplinary, etc. The journal especially encourages papers from new emerging and multidisciplinary areas, as well as papers reflecting the international trends of research and development and on special topics reporting progress made by Chinese computer scientists.