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A novel demand prefetching algorithm based on Volterra adaptive prediction for virtual memory management systems

机译：一种基于Volterra自适应预测的虚拟内存管理系统需求预取算法

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The performance of a virtual memory system is the result of the goodness of the memory management policy. The 'demand fetch' policy is one of the most popular, mainly for its simplicity. However, at the expense of increased complexity, other policies can be devised. In this paper, a novel approach with a relatively low complexity is described for the determination of a suitable set of pages to be brought into memory when a page fault occurs. This algorithm is an example of how the overall performance of complex systems can be improved with little computational effort. To anticipate their future use, some pages are determined by using a nonlinear predictor based on the truncated Volterra series. The Volterra predictor is updated every time a new page reference comes in. We first give experimental evidence that page reference sequences contain nonlinearities which can be described using a Volterra predictor. Then we show how the predictor's performance is improved by exploiting temporal and spatial localities in the trace on the basis of the page references histogram and with an input LRU stack filter. When a page fault occurs, a number of pages around the predicted page are brought into memory, in addition to the page which caused the page fault, replacing the pages chosen on an LRU basis in the same section. Trace-driven simulations show that this algorithm leads to a page fault improvement of as much as 10.9% with respect to a conventional demand paging algorithm with the same dimension of the working set (WS). Some results in terms of page fault rate vs. WS dimension are reported.
机译：虚拟内存系统的性能是内存管理策略良好的结果。 “取指令”策略是最受欢迎的策略之一，主要是因为其简单性。但是，以增加复杂性为代价，可以设计其他策略。在本文中，描述了一种复杂度相对较低的新颖方法，用于确定发生页面错误时要带入内存的一组合适的页面。该算法是如何以较少的计算量来改善复杂系统的整体性能的示例。为了预测其将来的使用，通过使用基于截断的Volterra级数的非线性预测器来确定一些页面。每当有新的页面引用进入时，Volterra预测器都会更新。我们首先提供实验证据，表明页面参考序列包含可以使用Volterra预测器描述的非线性。然后，我们展示了如何通过在页面引用直方图的基础上并使用输入LRU堆栈过滤器来利用迹线中的时间和空间局部性来改善预测器的性能。当发生页面错误时，除了导致页面错误的页面外，还将在预测页面周围的许多页面带入内存，替换了在同一部分中基于LRU选择的页面。跟踪驱动的仿真表明，与具有相同工作集（WS）尺寸的常规需求分页算法相比，该算法可将页面错误改进多达10.9％。报告了一些有关页面错误率与WS尺寸的结果。

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