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Coordinated placement and replacement for large-scale distributed caches

机译:大规模分布式缓存的协调放置和替换

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摘要

In a large-scale information system such as a digital library or the Web, a set of distributed caches can improve their effectiveness by coordinating their data placement decisions. Using simulation, we examine three practical cooperative placement algorithms, including one that is provably close to optimal, and we compare these algorithms to the optimal placement algorithm and several cooperative and noncooperative replacement algorithms. We draw five conclusions from these experiments: 1) cooperative placement can significantly improve performance compared to local replacement algorithms, particularly when the size of individual caches is limited compared to the universe of objects; 2) although the amortizing placement algorithm is only guaranteed to be within 14 times the optimal, in practice it seems to provide an excellent approximation of the optimal; 3) in a cooperative caching scenario, the recent greedy-dual local replacement algorithm performs much better than the other local replacement algorithms; 4) our hierarchical-greedy-dual replacement algorithm yields further improvements over the greedy-dual algorithm especially when there are idle caches in the system; and 5) a key challenge to coordinated placement algorithms is generating good predictions of access patterns based on past accesses.
机译:在诸如数字图书馆或Web之类的大规模信息系统中,一组分布式缓存可以通过协调其数据放置决策来提高其有效性。通过仿真,我们研究了三种实用的协同放置算法,包括一种可证明接近最优的算法,并将这些算法与最佳放置算法以及几种协同和非协同替换算法进行了比较。我们从这些实验中得出五个结论:1)与局部替换算法相比,协同放置可以显着提高性能,尤其是当单个缓存的大小与对象范围相比有限时; 2)尽管摊销算法仅保证在最优值的14倍以内,但实际上它提供了最优值的极佳近似值; 3)在协作缓存的情况下,最近的贪婪对偶本地替换算法的性能要比其他本地替换算法好得多; 4)我们的分层贪婪对偶替换算法比贪婪对偶算法产生了进一步的改进,尤其是当系统中有空闲缓存时; 5)协调放置算法的主要挑战是根据过去的访问情况生成对访问模式的良好预测。

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