As emerging memory technologies (e.g., non-volatile memory (NVM)) coming out and machine learning algorithms successfully applying to different fields, the potentials of cache replacement policy for NVM-based systems with the integration of machine learning algorithms are worthy of being exploited to improve the performance of computer systems. In this work, we proposed a machine learning based cache replacement algorithm, named ExpCache, to improve the system performance with NVM as the main memory. By considering the non-volatility characteristic of the NVM devices, we split the whole NVM into two caches, including a read cache and a write cache, for retaining different types of requests. The pages in each cache are managed by both LRU and LFU policies for balancing the recency and frequency of workloads. The online Expert machine learning algorithm is responsible for selecting a proper policy to evict a page from one of the caches based on the access patterns of workloads. In experimental results, the proposed ExpCache outperforms previous studies in terms of hit ratio and the number of dirty pages written back to storage.
|Original language||English (US)|
|Title of host publication||Proceedings - 2022 International Conference on Compilers, Architecture, and Synthesis for Embedded Systems, CASES 2022|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||2|
|State||Published - 2022|
|Event||2022 International Conference on Compilers, Architecture, and Synthesis for Embedded Systems, CASES 2022 - Shanghai, China|
Duration: Oct 7 2022 → Oct 14 2022
|Name||Proceedings - 2022 International Conference on Compilers, Architecture, and Synthesis for Embedded Systems, CASES 2022|
|Conference||2022 International Conference on Compilers, Architecture, and Synthesis for Embedded Systems, CASES 2022|
|Period||10/7/22 → 10/14/22|
Bibliographical noteFunding Information:
V. ACKNOWLEDGEMENT This work was partially supported by NSF I/UCRC Center Research in Intelligent Storage and the following NSF awards 1439622, 1812537, 2204656, and 2204657. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.
© 2022 IEEE.
- Cache policy
- Non-volatile memory
- Online learning