论文标题
带有预测的加权分页的在线算法
Online Algorithms for Weighted Paging with Predictions
论文作者
论文摘要
在本文中，我们启动了对预测的加权分页问题的研究。这延续了在线算法中的最新作品，并进行了预测，尤其是Lykouris和Vassilvitski（ICML 2018）和Rohatgi（苏打水2020年）对未加权的分页进行预测。我们表明，与未加权的分页不同，固定的lookahead或对每个页面的下一个请求都不是足够的信息来克服加权分页中现有的下限。但是，这两个组合，我们称之为每个请求预测（SPRP）模型，足以给出2个竞争力的算法。我们还探讨了随着预测误差的增加而优雅地退化算法的问题，并为一组自然预测误差的自然测量提供了上限和下限。
In this paper, we initiate the study of the weighted paging problem with predictions. This continues the recent line of work in online algorithms with predictions, particularly that of Lykouris and Vassilvitski (ICML 2018) and Rohatgi (SODA 2020) on unweighted paging with predictions. We show that unlike unweighted paging, neither a fixed lookahead nor knowledge of the next request for every page is sufficient information for an algorithm to overcome existing lower bounds in weighted paging. However, a combination of the two, which we call the strong per request prediction (SPRP) model, suffices to give a 2-competitive algorithm. We also explore the question of gracefully degrading algorithms with increasing prediction error, and give both upper and lower bounds for a set of natural measures of prediction error.
