The design and implementation of a log-structured file system
SOSP '91 Proceedings of the thirteenth ACM symposium on Operating systems principles
Using data clustering to improve cleaning performance for plash memory
Software—Practice & Experience
An Adaptive Striping Architecture for Flash Memory Storage Systems of Embedded Systems
RTAS '02 Proceedings of the Eighth IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS'02)
Efficient identification of hot data for flash memory storage systems
ACM Transactions on Storage (TOS)
Design tradeoffs for SSD performance
ATC'08 USENIX 2008 Annual Technical Conference on Annual Technical Conference
Proceedings of the 14th international conference on Architectural support for programming languages and operating systems
Write amplification analysis in flash-based solid state drives
SYSTOR '09 Proceedings of SYSTOR 2009: The Israeli Experimental Systems Conference
Hot data identification for flash-based storage systems using multiple bloom filters
MSST '11 Proceedings of the 2011 IEEE 27th Symposium on Mass Storage Systems and Technologies
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Separating hot data from cold data is known to allow for efficient management of NAND flash memory in Solid State Drives (SSDs). However, most of previous work has been evaluated with the trace-driven simulations under different workloads and testing conditions. The goal of this paper is to empirically study the performance, computation overhead, and memory consumption of the existing hot/cold data separation policies on a real SSD platform. After devising a general framework where a different policy can be easily plugged in, we have evaluated three hot/cold data separation policies: 2-level LRU (LRU), Multiple Bloom Filter (MBF), and Dynamic dAta Clustering (DAC). Our evaluation results show that DAC performs best, improving the performance by up to 58% in real workloads with a reasonable computation and memory overhead.