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Date of Publication:2022-10-10

Journal:计算机学报

Issue:7

Page Number:1538-1547

ISSN No.:0254-4164

Abstract:In practical application, the existing LRFU self-adaptive replacement algorithms adjust the λ value based on experience and lack quantitative analysis of access locality strength. Consequently, the access patterns these algorithms can be applicable for are limited. Firstly the locality quantitative analysis model is created through K-order Markov Chain (K→∞), and in the access course the model real-timely quantizes the locality strength in accordance with the statistical information. Then the self-adaptive replacement algorithm called LA-LRFU (Locality-Aware LRFU) is designed based on the analysis model. As the access feature changes, the algorithm dynamically adjusts the λ value correspondingly. Finally the LA-LRFU is tested under the trace simulations. The results shows that, for several access patterns LA-LRFU can significantly improve the cache hit rate. And during the practical access process consisting of several different patterns, the LA-LRFU can adjust the λ value more rationally than other LRFU self-adaptive replacement algorithms.

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