Utilizing the Perceived Age to Maximize Freshness in Query-Based Update Systems

TL;DR

This paper develops optimal sampling strategies for monitoring continuous-time Markov chains using query-based sampling, considering realistic delay distributions and feedback times to improve data freshness.

Contribution

It relaxes common assumptions of exponential delays and instant feedback, proposing waiting-based strategies that enhance mean binary freshness in monitoring systems.

Findings

Waiting-based sampling strategies significantly improve data freshness.

Optimal policies are derived under generic delay distributions.

Relaxing assumptions leads to more realistic and effective monitoring methods.

Abstract

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.