Abstract
Recently, adopting large cache blocks has received widespread attention in server-side storage caching. Besides reducing the management overheads of cache blocks, it can significantly boost the I/O throughput. However, although using large blocks has advantages in management overhead and I/O performance, existing fixed-size block management schemes in storage cache cannot effectively handle them under the complicated real-world workloads. We find that existing fixed-size block management methods will suffer from the fragmentation within the cache block and fail to identify hot/cold cache blocks correctly when adopting large blocks for caching.
Therefore, aiming to solve this problem, we propose AIR cache, which is a variable-size block cache based on fine-grained management method. AIR cache contains three major parts, Multi-Granularity Writer (MGW), Multi-Granularity Eviction (MGE) and Fine-Grained Recorder (FGR) where FGR is dedicated to record the data popularity using fine-grained data sections, MGW writes data at different granularity, and MGE is responsible for evicting the data at dynamic granularity. Our experiments with real-world traces demonstrate that AIR cache can increase the read cache hit ratio by up to 6.97X and the cache space utilization rate by up to 3.63X over the traditional fixed-size block management methods.
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Acknowledgements
This work was supported by grants from Natural Science Foundation of China No. 62072059, Open Project Program of Wuhan National Laboratory for Optoelectronics No. 2019WNLOKF009, Natural Science Foundation of Chongqing No. cstc2020jcyj-msxmX0897, the Fundamental Research Funds for the Central Universities No. 2020CDJLHZZ-050.
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Li, Y. et al. (2021). AIR Cache: A Variable-Size Block Cache Based on Fine-Grained Management Method. In: U, L.H., Spaniol, M., Sakurai, Y., Chen, J. (eds) Web and Big Data. APWeb-WAIM 2021. Lecture Notes in Computer Science(), vol 12859. Springer, Cham. https://doi.org/10.1007/978-3-030-85899-5_12
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