Can Determinacy Minimize Age of Information?

TL;DR

This paper investigates whether deterministic update generation and service times optimize Age-of-Information (AoI), revealing that in some queueing systems, deterministic service can worsen AoI while heavy-tailed distributions can minimize it, highlighting a fundamental difference from delay metrics.

Contribution

The paper demonstrates that deterministic service does not always minimize AoI and that heavy-tailed distributions can achieve lower AoI, contrasting with traditional delay optimization.

Findings

Deterministic service can lead to worst-case AoI in certain systems.

Heavy-tailed service distributions can minimize AoI.

AoI optimization differs fundamentally from delay minimization.

Abstract

Age-of-information (AoI) is a newly proposed performance metric of information freshness. It differs from the traditional delay metric, because it is destination centric and measures the time that elapsed since the last received fresh information update was generated at the source. AoI has been analyzed for several queueing models, and the problem of optimizing AoI over arrival and service rates has been studied in the literature. We consider the problem of minimizing AoI over the space of update generation and service time distributions. In particular, we ask whether determinacy, i.e. periodic generation of update packets and/or deterministic service, optimizes AoI. By considering several queueing systems, we show that in certain settings, deterministic service can in fact result in the worst case AoI, while a heavy-tailed distributed service can yield the minimum AoI. This leads to an interesting conclusion that, in some queueing systems, the service time distribution that minimizes expected packet delay, or variance in packet delay can, in fact, result in the worst case AoI. This exposes a fundamental difference between AoI metrics and packet delay.