Work-in-Progress: ExpCache: Online-Learning based Cache Replacement Policy for Non-Volatile Memory
Work-in-Progress: ExpCache: Online-Learning based Cache Replacement Policy for Non-Volatile Memory
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正在进行中的工作：ExpCache：基于在线学习的非易失性内存缓存替换策略
DOI：
10.1109/cases55004.2022.00010
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Lilja, David
中科院分区：
文献类型：
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作者：
Yang, Jinfeng;Li, Bingzhe;Yuan, Jianjun;Shen, Zhaoyan;Du, David;Lilja, David
随着新兴的存储技术(如非易失性存储器)的出现和机器学习算法在不同领域的成功应用，基于非易失性存储器系统的缓存替换策略与机器学习算法相结合的潜力值得挖掘，以提高计算机系统的性能。在这项工作中，我们提出了一种基于机器学习的缓存替换算法ExpCache，以提高NVM作为主存的系统性能。考虑到NVM设备的非易失性，我们将整个NVM划分为两个缓存，包括读缓存和写缓存，用于保存不同类型的请求。每个缓存中的页面都由LRU和LFU策略进行管理，以平衡工作负载的新近性和频率。在线专家机器学习算法负责根据工作负载的访问模式选择适当的策略从其中一个缓存中驱逐页面。实验结果表明，在命中率和写回存储的脏页数量方面，ExpCach的性能优于以往的研究。
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.
HeuristicDB：使用非易失性内存块设备的混合存储数据库系统
DOI：
--
发表时间：
2021
期刊：
Annual Haifa Experimental Systems Conference
影响因子：
--
作者：
Jinfeng Yang;Bingzhe Li;D. Lilja
通讯作者：
D. Lilja