Query-based sampling has become an increasingly popular technique for monitoring Markov sources in pull-based update systems. However, most of the contemporary literature on this assumes an exponential distribution for query delay and often relies on the assumption that the feedback or replies to the queries are instantaneous. In this work, we relax both of these assumptions and find optimal sampling policies for monitoring continuous-time Markov chains (CTMC) under generic delay distributions. In particular, we show that one can obtain significant gains in terms of mean binary freshness (MBF) by employing a waiting based strategy for query-based sampling.

翻译：基于查询的采样已成为拉式更新系统中监控马尔可夫源的一项日益流行的技术。然而，当代文献大多假设查询延迟服从指数分布，并常依赖于查询反馈或回复是瞬时完成的假设。在本工作中，我们放宽了这两个假设，并在一般延迟分布下寻找监控连续时间马尔可夫链的最优采样策略。特别地，我们证明了通过采用基于等待的查询采样策略，可以在平均二元新鲜度方面获得显著提升。