Device state recovery in non-volatile main memory systems

Ohmura, Ren; Yamasaki, Nobuyuki; Anzai, Yuichiro

doi:10.1109/cmpsac.2003.1245316

Cited by 3 publications

(1 citation statement)

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“…Device recovery Our WSP prototype takes a simple approach to saving and recovering device state, which takes much longer than flushing processor caches and contexts. Ohmura et al [25] propose an alternative approach: shadowing device registers in NVRAM by logging register writes on each device operation. This removes eliminates the "state save" at the cost of additional complexity in the device driver.…”

Section: Related Workmentioning

confidence: 99%

Whole-system persistence

NarayananDushyanth¹,

HodsonOrion

2012

SIGPLAN Not.

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Today's databases and key-value stores commonly keep all their data in main memory. A single server can have over 100 GB of memory, and a cluster of such servers can have 10s to 100s of TB. However, a storage back end is still required for recovery from failures. Recovery can last for minutes for a single server or hours for a whole cluster, causing heavy load on the back end. Nonvolatile main memory (NVRAM) technologies can help by allowing near-instantaneous recovery of in-memory state. However, today's software does not support this well. Block-based approaches such as persistent buffer caches suffer from data duplication and block transfer overheads. Recently, user-level persistent heaps have been shown to have much better performance than these. However they require substantial application modification and still have significant runtime overheads.This paper proposes whole-system persistence (WSP) as an alternative. WSP is aimed at systems where all memory is nonvolatile. It transparently recovers an application's entire state, making a failure appear as a suspend/resume event. Runtime overheads are eliminated by using "flush on fail": transient state in processor registers and caches is flushed to NVRAM only on failure, using the residual energy from the system power supply. Our evaluation shows that this approach has 1.6-13 times better runtime performance than a persistent heap, and that flush-on-fail can complete safely within 2-35% of the residual energy window provided by standard power supplies.

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Section: Related Workmentioning

confidence: 99%