Data Persistence

2009 ◽  
pp. 203-240
Keyword(s):  
Data Persistence

Research of Hibernate frame-based data persistence and its application

2009 ◽  
Vol 28 (9) ◽  
pp. 2446-2448 ◽  
Author(s):  
Yun XIA ◽  
Zhi-shu LI
Keyword(s):  
Data Persistence

Gem #20: using pragma shared_passive for data persistence

2008 ◽  
Vol 28 (2) ◽  
pp. 40-41
Author(s):  
Pascal Obry
Keyword(s):  
Data Persistence

Data persistence in sensor networks

2005 ◽  
Vol 33 (2) ◽  
pp. 24-26 ◽  
Author(s):  
Abhinav Kamra ◽  
Jon Feldman ◽  
Vishal Misra ◽  
Dan Rubenstein
Keyword(s):  
Sensor Networks ◽  
Data Persistence

NVLSM: A Persistent Memory Key-Value Store Using Log-Structured Merge Tree with Accumulative Compaction

2021 ◽  
Vol 17 (3) ◽  
pp. 1-26
Author(s):  
Baoquan Zhang ◽  
David H. C. Du
Keyword(s):  
Computer Systems ◽  
Memory Storage ◽  
Low Latency ◽  
Latency Data ◽  
Persistent Memory ◽  
Write Amplification ◽  
Non Volatile Memory ◽  
Data Persistence ◽  
Average Latency ◽  
Volatile Memory

Computer systems utilizing byte-addressable Non-Volatile Memory ( NVM ) as memory/storage can provide low-latency data persistence. The widely used key-value stores using Log-Structured Merge Tree ( LSM-Tree ) are still beneficial for NVM systems in aspects of the space and write efficiency. However, the significant write amplification introduced by the leveled compaction of LSM-Tree degrades the write performance of the key-value store and shortens the lifetime of the NVM devices. The existing studies propose new compaction methods to reduce write amplification. Unfortunately, they result in a relatively large read amplification. In this article, we propose NVLSM, a key-value store for NVM systems using LSM-Tree with new accumulative compaction. By fully utilizing the byte-addressability of NVM, accumulative compaction uses pointers to accumulate data into multiple floors in a logically sorted run to reduce the number of compactions required. We have also proposed a cascading searching scheme for reads among the multiple floors to reduce read amplification. Therefore, NVLSM reduces write amplification with small increases in read amplification. We compare NVLSM with key-value stores using LSM-Tree with two other compaction methods: leveled compaction and fragmented compaction. Our evaluations show that NVLSM reduces write amplification by up to 67% compared with LSM-Tree using leveled compaction without significantly increasing the read amplification. In write-intensive workloads, NVLSM reduces the average latency by 15.73%–41.2% compared to other key-value stores.

Survive: Pointer-Based In-DRAM Incremental Checkpointing for Low-Cost Data Persistence and Rollback-Recovery

2017 ◽  
Vol 16 (2) ◽  
pp. 153-157 ◽  
Author(s):  
Amirhossein Mirhosseini ◽  
Aditya Agrawal ◽  
Josep Torrellas
Keyword(s):  
Low Cost ◽  
Cost Data ◽  
Rollback Recovery ◽  
Data Persistence

scholarly journals Managing Data Persistence in Network Enabled Servers

2005 ◽  
Vol 13 (4) ◽  
pp. 333-354 ◽  
Author(s):  
Eddy Caron ◽  
Bruno DelFabbro ◽  
Frédéric Desprez ◽  
Emmanuel Jeannot ◽  
Jean-Marc Nicod
Keyword(s):  
Data Management ◽  
Experimental Results ◽  
Model Data ◽  
Client Server ◽  
University Of Tennessee ◽  
Communication Time ◽  
Data Persistence ◽  
Remote Client ◽  
The University ◽  
Global Grid

The GridRPC model [17] is an emerging standard promoted by the Global Grid Forum (GGF) that defines how to perform remote client-server computations on a distributed architecture. In this model data are sent back to the client at the end of every computation. This implies unnecessary communications when computed data are needed by an other server in further computations. Since, communication time is sometimes the dominant cost of remote computations, this cost has to be lowered. Several tools instantiate the GridRPC model such as NetSolve developed at the University of Tennessee, Knoxville, USA, and DIET developed at LIP laboratory, ENS Lyon, France. They are usually called Network Enabled Servers (NES). In this paper, we present a discussion of the data management solutions chosen for these two NES (NetSolve and DIET) as well as experimental results.

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