HDCat: Effectively Identifying Hot Data in Large-Scale I/O Streams with Enhanced Temporal Locality

Jiahao Chen, Yuhui Deng, Zhan Huang

Abstract: Hot data is very important for optimizing modern computer systems. For example, the identified hot data can be employed to extend the lifespan of flash memory. However, it is very challenging to effectively identify hot data with low memory consumption and low runtime overhead. This paper proposes a Hot Data Catcher (HDCat) which can effectively identify hot data in large-scale I/O streams by leveraging enhanced temporal locality. HDCat only maintains a hot data queue and a candidate hot data queue to record the data access pattern by tracking limited data set, thus effectively reducing the memory consumption. Furthermore, HDCat adopts a D-bit counter and a recency-bit to leverage both the frequency and recency contained in the data stream. Additionally, HDCat can significantly reduce the conversion between hot data and cold data. Real traces are used to evaluate the proposed approach. Experimental results demonstrate that HDCat significantly outperforms the state-of-the-art Multi-hash algorithm and the two-level LRU algorithm.