Persistent State Machines for Recoverable In-memory Storage Systems with NVRam


Wen Zhang, UC Berkeley; Scott Shenker, UC Berkeley/ICSI; Irene Zhang, Microsoft Research/University of Washington


Distributed in-memory storage systems are crucial for meeting the low latency requirements of modern datacenter services. However, they lose all state on failure, so recovery is expensive and data loss is always a risk. Persistent memory (PM) offers the possibility of building fast, persistent in-memory storage; however, existing PM systems are built from scratch or require heavy modification of existing systems. To rectify these problems, this paper presents Persimmon, a PM-based system that converts existing distributed in-memory storage systems into persistent, crash-consistent versions with low overhead and minimal code changes.

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@inproceedings {258927,
author = {Wen Zhang and Scott Shenker and Irene Zhang},
title = {Persistent State Machines for Recoverable In-memory Storage Systems with NVRam},
booktitle = {14th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 20)},
year = {2020},
isbn = {978-1-939133-19-9},
pages = {1029--1046},
url = {},
publisher = {{USENIX} Association},
month = nov,

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