B2L: A hot data identification algorithm by fusing bloom filter and temporal locality for NAND flash based solid-state drives

Abstract

Aiming at improving the firmware performance of NAND flash based solid-state drives, this paper proposes a hot data identification algorithm by fusing bloom filter and temporal locality, briefly as B2L, whose main contribution of B2L is to use cascade structure to make it have both advantages. Specifically. Firstly, the bloom filter is used to filter the real cold data, so that the probability of the remaining data being hot data becomes higher. Secondly, a temporal locality based two-level least recently used (T-LRU) lists is used to identify the true hot data. That is to say, the data hit in the hot list of T-LRU are identified as the true hot data. B2L can avoid the high false positive ratio problem of the bloom filter and effectively reduce the false positive ratio of T-LRU and the false negative ratio caused by false positives. Hence, B2L can improve the accuracy of hot data identification. The experimental result shows that in the case of using the direct address method as the baseline, compared with the state-of-the- art identification algorithms, B2L improves the accuracy of hot data identification by 60.4% on average.