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

2026-01-20

AI Summary

This work addresses the limitations of existing query-based updating systems, which typically assume exponentially distributed query delays and instantaneous feedback—assumptions that often fail to reflect real-world conditions. To overcome these constraints, the study investigates the design of optimal query sampling policies for continuous-time Markov chains under general delay distributions and non-instantaneous feedback, with the goal of enhancing information freshness. The authors propose a waiting-based sampling mechanism and leverage continuous-time Markov modeling combined with optimization theory to derive a policy that maximizes Mean Binary Freshness (MBF). Experimental results demonstrate that the proposed approach consistently achieves significant improvements in information freshness across a variety of delay distributions, thereby confirming its effectiveness and broad applicability.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Data Mining & Knowledge Management: Intelligent Query Processing
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Web Mining and Content Analysis: Content-based information diffusion
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• query-based sampling
• age of information
• continuous-time Markov chains
• non-exponential delay
• freshness

Innovation

• query-based sampling
• waiting strategy
• mean binary freshness
• generic delay distributions
• continuous-time Markov chains