TY - JOUR
T1 - HeterMM
T2 - applying in-DRAM index to heterogeneous memory-based key-value stores
AU - Ji, Yunhong
AU - Huang, Wentao
AU - Zhou, Xuan
N1 - Publisher Copyright:
© Higher Education Press 2024.
PY - 2024/8
Y1 - 2024/8
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85189621446
U2 - 10.1007/s11704-024-3713-0
DO - 10.1007/s11704-024-3713-0
M3 - 快报
AN - SCOPUS:85189621446
SN - 2095-2228
VL - 18
JO - Frontiers of Computer Science
JF - Frontiers of Computer Science
IS - 4
M1 - 184612
ER -