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.
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Acknowledgments
This work is supported by the National Natural Science Foundation (NSF) of China under Grant (No. 61572232, and No. 61272073), the key program of Natural Science Foundation of Guangdong Province (No. S2013020012865), the Open Research Fund of Key Laboratory of Computer System and Architecture, Institute of Computing Technology, Chinese Academy of Sciences (CARCH201401), and the Fundamental Research Funds for the Central Universities, and the Science and Technology Planning Project of Guangdong Province (No. 2013B090200021).
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Chen, J., Deng, Y., Huang, Z. (2015). HDCat: Effectively Identifying Hot Data in Large-Scale I/O Streams with Enhanced Temporal Locality. In: Wang, G., Zomaya, A., Martinez, G., Li, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science(), vol 9529. Springer, Cham. https://doi.org/10.1007/978-3-319-27122-4_9
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DOI: https://doi.org/10.1007/978-3-319-27122-4_9
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