Cache advertisements reduce the access cost by allowing users to skip the cache when it does not contain their datum. Such advertisements are used in multiple networked domains such as 5G networks, wide area networks, and information-centric networking. The selection of an advertisement strategy exposes a trade-off between the access cost and bandwidth consumption. Still, existing works mostly apply a trial-and-error approach for selecting the best strategy, as the rigorous foundations required for optimizing such decisions is lacking. Our work shows that the desired advertisement policy depends on numerous parameters such as the cache policy, the workload, the cache size, and the available bandwidth. In particular, we show that there is no ideal single configuration. Therefore, we design an adaptive, self-adjusting algorithm that periodically selects an advertisement policy. Our algorithm does not require any prior information about the cache policy, cache size, or workload, and does not require any apriori configuration. Through extensive simulations, using several state-of-the-art cache policies, and real workloads, we show that our approach attains a similar cost to that of the best static configuration (which is only identified in retrospect) in each case.
翻译:Cache 广告降低了访问成本, 允许用户在不包含其数据时跳过缓存。 这些广告被用于多个网络域, 如 5G 网络、 广域网和以信息为中心的网络 。 选择广告策略暴露了访问成本和带宽消耗之间的权衡。 但是, 现有的工程大多采用试一试的方法来选择最佳战略, 因为优化这些决策所需的严格基础缺乏。 我们的工作表明, 想要的广告政策取决于许多参数, 如缓存政策、 工作量、 缓存大小 和可用带宽。 特别是, 我们显示, 我们没有理想的单一配置。 因此, 我们设计了适应性、 自调整算法, 定期选择广告政策。 我们的算法不需要任何关于缓存政策、 缓存大小或工作量的先前信息, 也不需要任何原始配置 。 通过广泛的模拟, 使用一些最先进的缓存政策, 以及实际工作量, 我们的计算方法在每种情况下都达到了与最佳静态配置( 仅在回镜中确定) 相似的成本 。