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首页> 外文期刊>IEEE Transactions on Computers >Making LRU friendly to weak locality workloads: a novel replacement algorithm to improve buffer cache performance
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Making LRU friendly to weak locality workloads: a novel replacement algorithm to improve buffer cache performance

机译:使LRU对弱本地工作负载友好:一种新颖的替换算法,可提高缓冲区高速缓存的性能

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摘要

Although the LRU replacement algorithm has been widely used in buffer cache management, it is well-known for its inability to cope with access patterns with weak locality. Previously proposed algorithms to improve LRU greatly increase complexity and/or cannot provide consistently improved performance. Some of the algorithms only address LRU problems on certain specific and predefined cases. Motivated by the limitations of existing algorithms, we propose a general and efficient replacement algorithm, called Low Inter-reference Recency Set (LIRS). LIRS effectively addresses the limitations of LRU by using recency to evaluate Inter-Reference Recency (IRR) of accessed blocks for making a replacement decision. This is in contrast to what LRU does: directly using recency to predict the next reference time. Meanwhile, LIRS mostly retains the simple assumption adopted by LRU for predicting future block access behaviors. Conducting simulations with a variety of traces of different access patterns and with a wide range of cache sizes, we show that LIRS significantly outperforms LRU and outperforms other existing replacement algorithms in most cases. Furthermore, we show that the additional cost for implementing LIRS is trivial in comparison with that of LRU. We also show that the LIRS algorithm can be extended into a family of replacement algorithms, in which LRU is a special member.
机译:尽管LRU替换算法已广泛用于缓冲区高速缓存管理中,但它因无法处理局部性较弱的访问模式而闻名。先前提出的用于改善LRU的算法极大地增加了复杂性和/或无法提供持续改进的性能。一些算法仅在某些特定和预定义的情况下解决LRU问题。由于现有算法的局限性,我们提出了一种通用且有效的替换算法,称为低参考间新近度集(LIRS)。 LIRS通过使用新近度来评估所访问块的参考间新近度(IRR)以做出替换决策,从而有效地解决了LRU的局限性。这与LRU所做的相反:直接使用新近度来预测下一个参考时间。同时,LIRS大多保留了LRU用来预测未来块访问行为的简单假设。通过对各种不同访问模式的痕迹和各种高速缓存大小进行仿真,我们表明,在大多数情况下,LIRS的性能明显优于LRU,并且性能也优于其他现有的替换算法。此外,我们证明,与LRU相比,实施LIRS的额外成本微不足道。我们还表明,LIRS算法可以扩展到LRU是其中一个特殊成员的替换算法家族中。

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