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Hotness-aware page partition management method

  • S.I. : Machine Learning Applications for Self-Organized Wireless Networks
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Abstract

The era of big data is here, the demand of mass data for the storage and processing ability of computer system is bigger and bigger. The computer’s information process ability is strong enough; however, the performance of computer storage system has not improved much. In this paper, we use the DRAM and PCM to build mixed main memory and use the SSD and HDD to build secondary storage to build a hybrid storage system. Aiming at the hit rate in hybrid main memory and the writing life of PCM, a hotness-aware page management algorithm is proposed. We research the hybrid memory architecture based on PCM and DRAM, and we propose a page partition management method based on heat perception. We use the operating mechanism that is similar with traditional CLOCK algorithm to ensure the system hit rate. And we lead into the recently twice concept of writing distance and combine with the page history information to accurately judge the hot or cold of pages. Then, we design the page migration management mechanism. By writing clock linked list to track the page writing heat dynamic, we move the hot page to DRAM. And, we reduce the number of PCM write to improve the life of PCM. Finally, it is verified by the simulation experiments that this method reduces the number of write times on PCM by 9.5%, while ensuring the hit rate.

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Acknowledgements

The work has been supported by the National Natural Science Foundation of China (No. 61672004) and the Chongqing Research Program of Basic Research and Frontier Technology under Grant NO. cstc2016jcyjA0590.

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Authors and Affiliations

  1. College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Fengjun Shang, Chang Liu & Wenkai Wang

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  1. Fengjun Shang
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  2. Chang Liu
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  3. Wenkai Wang
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Correspondence to Fengjun Shang.

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Shang, F., Liu, C. & Wang, W. Hotness-aware page partition management method. Neural Comput & Applic 31 (Suppl 1), 133–146 (2019). https://doi.org/10.1007/s00521-018-3668-x

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  • DOI: https://doi.org/10.1007/s00521-018-3668-x

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