论文标题
学习平板课程以减轻备忘录中的记忆孔
Learning Slab Classes to Alleviate Memory Holes in Memcached
论文作者
论文摘要
我们考虑了平板分配器中的内存孔的问题，由于最近较大的板级尺寸和输入项目的大小之间的差异，其中输入内存的项目所占据的内存比实际要求的要多。我们通过使用贪婪的算法来解决此问题，该算法分析先前输入内存中的项目的大小，并因此重新配置默认的平板类，以更好地适合学习的流量模式，以最大程度地减少内存孔。在我们的发现中，使用这种方法来实现一致的数据模式，在记忆浪费中已大大减少。我们认为已被备忘录，因为它是当今平板分配器中使用最广泛的实现之一，并具有重新配置其默认平板类的本机支持。
We consider the problem of memory holes in slab allocators, where an item entered into memory occupies more memory than it actually requires due to a difference between the nearest larger slab class size and the size of the entered item. We solve this problem by using a greedy algorithm that analyses the pattern of the sizes of items previously entered into the memory and accordingly re-configuring the default slab classes to better suit the learned traffic pattern to minimize memory holes. Using this approach for a consistent data pattern, in our findings, has yielded significant reductions in memory wastage. We consider Memcached as it is one of the most widely used implementations of slab allocators today, and has native support to reconfigure its default slab classes.
