计算机科学
隐藏物
缓存污染
缓存着色
页面缓存
智能缓存
缓存失效
缓存算法
缓存不经意算法
操作系统
分布式计算
并行计算
CPU缓存
作者
Rong Gu,Chongjie Li,Haipeng Dai,Yili Luo,Xiaolong Xu,Shaohua Wan,Yihua Huang
标识
DOI:10.1016/j.sysarc.2021.101994
摘要
Nowadays, as the memory capacity of servers become larger and larger, distributed in-memory file systems, which enable applications to interact with data at fast speed, have been widely used. However, the existing distributed in-memory file systems still face the problem of low data access performance in small data reading, which seriously reduce their usefulness in many important big data scenarios. In this paper, we analyze the factors that affect the performance of reading in-memory files and propose a two-layer user space cache management mechanism: in the first layer, we cache data packet references to reduce frequent page fault interruptions (packet-level cache); in the second layer, we cache and manage small file data units to avoid redundant inter-process communications (object-level cache). We further design a fine-grained caching model based on the submodular function optimization theory, for efficiently managing the variable-length cache units with partially overlapping fragments on the client side. Experimental results on synthetic and real-world workloads show that compared with the existing cutting-edge systems, the first level cache can double the reading performance on average, and the second level cache can improve random reading performance by more than 4 times. Our caching strategies also outperform the cutting-edge cache algorithms over 20% on hit ratio. Furthermore, the proposed client-side caching framework idea has been adopted by the Alluxio open source community, which shows the practical benefits of this work. • A packet-level cache policy for reducing page faults during sequential data reading. • An in-memory cache layer to cache the variable-length hot file fragments. • Proved that the variable-length cache problem is a submodular optimization problem theoretically. • Comprehensive performance evaluation of the client-side cache strategies.
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