A Novel Demand Prefetching Algorithm Based on Volterra Adaptive Prediction for Virtual Memory Management Systems
Proceedings of the Thirtieth Hawaii International Conference on System Sciences (1997)
Jan. 3, 1997 to Jan. 6, 1997
Enzo Mumolo , Universita' di Trieste, Via Valerio 10, 34127 Trieste
Giulia Bernardis , Universita' di Trieste, Via Valerio 10, 34127 Trieste
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 expenses of increased complexity, other policies can be devised. In this paper, a novel approach with a relatively low complexity for the determination of a suitable set of pages to be brought into memory when a page fault occurs is described. This algorithm is an example of how the overall performance of complex systems can be improved with low 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 non-linearities which can be described using a Volterra predictor. Then, we show how the predictor performance are 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. Some preliminar results in terms of page fault rate versus the WS dimension are finally reported.
G. Bernardis and E. Mumolo, "A Novel Demand Prefetching Algorithm Based on Volterra Adaptive Prediction for Virtual Memory Management Systems," 30th Hawaii International Conference on System Sciences (HICSS), Maui, Hawaii, 1997, pp. 160.