具有动态内存容量的分页
Paging with dynamic memory capacity
摘要：
我们研究了经典分页问题的泛化，它允许可用内存量随时间变化-捕获许多现代计算现实的基本属性，从云计算到多核和能源优化处理器。事实证明，当内存容量波动时，“经典”情况下的良好性能并不能保证性能：粗略地说，从静态容量到动态容量的变化可能意味着在空间和时间上最优性在2倍之内，而次优性在任意大的因子之内。更确切地说，采用竞争分析框架，我们表明，一些在线寻呼算法，尽管有一个最佳的（h，k）-竞争比时，容量保持不变，是不是（3，k）-竞争任何任意大的k在最小的容量波动的存在。从这个角度来看，令人惊讶的是，即使内存容量发生了不利的变化，几个经典的分页算法也能表现得非常好--即使没有显式地考虑这些变化！特别是，我们证明了LFD仍然达到了最小故障数，并且几个经典的在线算法，如LRU，具有“动态”（h，k）竞争比，这是最好的一个可以实现不知道未来的页面请求，即使一个人有完美的知识，未来的容量波动（该比率的精确特征表明，它几乎（尽管不完全）等于“经典”比率k/（k-h+1））。换句话说，通过仔细的管理，知道/预测未来的内存资源对性能的重要性远远低于知道/预测未来的数据访问。
英文摘要：
We study a generalization of the classic paging problem that allows the amount of available memory to vary over time - capturing a fundamental property of many modern computing realities, from cloud computing to multi-core and energy-optimized processors. It turns out that good performance in the "classic" case provides no performance guarantees when memory capacity fluctuates: roughly speaking, moving from static to dynamic capacity can mean the difference between optimality within a factor 2 in space and time, and suboptimality by an arbitrarily large factor. More precisely, adopting the competitive analysis framework, we show that some online paging algorithms, despite having an optimal (h,k)-competitive ratio when capacity remains constant, are not (3,k)-competitive for any arbitrarily large k in the presence of minimal capacity fluctuations. In this light it is surprising that several classic paging algorithms perform remarkably well even if memory capacity changes adversarially - even without taking those changes into explicit account! In particular, we prove that LFD still achieves the minimum number of faults, and that several classic online algorithms such as LRU have a "dynamic" (h,k)-competitive ratio that is the best one can achieve without knowledge of future page requests, even if one had perfect knowledge of future capacity fluctuations (an exact characterization of this ratio shows it is almost, albeit not quite, equal to the "classic" ratio k/(k-h+1)). In other words, with careful management, knowing/predicting future memory resources appears far less crucial to performance than knowing/predicting future data accesses.
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