Update or Wait: How to Keep Your Data Fresh

TL;DR

This paper investigates optimal strategies for maintaining data freshness in information update systems, revealing when the common zero-wait policy is suboptimal and proposing algorithms to find better policies based on age penalty functions.

Contribution

The paper introduces a general age penalty framework, formulates the problem as a semi-Markov decision process, and provides conditions under which the zero-wait policy is not optimal.

Findings

Zero-wait policy is suboptimal when age penalty grows quickly or transmission times are correlated or heavy-tailed.

Efficient algorithms are developed to find optimal update policies.

Conditions for the optimality of zero-wait policy are established.

Abstract

In this work, we study how to optimally manage the freshness of information updates sent from a source node to a destination via a channel. A proper metric for data freshness at the destination is the age-of-information, or simply age, which is defined as how old the freshest received update is since the moment that this update was generated at the source node (e.g., a sensor). A reasonable update policy is the zero-wait policy, i.e., the source node submits a fresh update once the previous update is delivered and the channel becomes free, which achieves the maximum throughput and the minimum delay. Surprisingly, this zero-wait policy does not always minimize the age. This counter-intuitive phenomenon motivates us to study how to optimally control information updates to keep the data fresh and to understand when the zero-wait policy is optimal. We introduce a general age penalty function to characterize the level of dissatisfaction on data staleness and formulate the average age penalty minimization problem as a constrained semi-Markov decision problem (SMDP) with an uncountable state space. We develop efficient algorithms to find the optimal update policy among all causal policies, and establish sufficient and necessary conditions for the optimality of the zero-wait policy. Our investigation shows that the zero-wait policy is far from the optimum if (i) the age penalty function grows quickly with respect to the age, (ii) the packet transmission times over the channel are positively correlated over time, or (iii) the packet transmission times are highly random (e.g., following a heavy-tail distribution).