Timely Processing Of Updates From Multiple Sources

TL;DR

This paper analyzes a publish-subscribe system where a decision process can reduce the age of decision updates by employing a lazy computation policy, offsetting high variance in computation time.

Contribution

It introduces a lazy computation policy that reduces the age of information in decision updates, even without controlling source update generation.

Findings

Lazy computation reduces average AoI at the monitor.

High variance in computation time negatively impacts AoI.

Lazy policy offsets variance effects to improve update freshness.

Abstract

We consider a system where the updates from independent sources are disseminated via a publish-subscribe mechanism. The sources are the publishers and a decision process (DP), acting as a subscriber, derives decision updates from the source data. We derive the stationary expected age of information (AoI) of decision updates delivered to a monitor. We show that a lazy computation policy in which the DP may sit idle before computing its next decision update can reduce the average AoI at the monitor even though the DP exerts no control over the generation of source updates. This AoI reduction is shown to occur because lazy computation can offset the negative effect of high variance in the computation time.