# Age and Value of Information Optimization for Systems with Multi-Class Updates

arXiv: https://arxiv.org/abs/2408.12589 · HTML: https://mlsanity.com/paper/2408.12589/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2024.

## Built on by (most cited first, 2 of 2)

- [Beyond Freshness and Semantics: A Coupon-Collector Framework for Effective Status Updates](https://mlsanity.com/paper/2603.26998/) (2026)
- [Joint Optimization of Training and Inference in Federated Edge Learning via Constrained Multi-Objective Deep Reinforcement Learning](https://mlsanity.com/paper/2605.25916/) (2026)

## Abstract (arXiv)

Received samples of a stochastic process are processed by a server for delivery as updates to a monitor. Each sample belongs to a class that specifies a distribution for its processing time and a function that describes how the value of the processed update decays with age at the monitor. The class of a sample is identified when the processed update is delivered. The server implements a form of M/G/1/1 blocking queue; samples arriving at a busy server are discarded and samples arriving at an idle server are subject to an admission policy that depends on the age and class of the prior delivered update. For the delivered updates, we characterize the average age of information (AoI) and average value of information (VoI). We derive the optimal stationary policy that minimizes the convex combination of the AoI and (negative) VoI. It is shown that the policy has a threshold structure, in which a new sample is allowed to arrive to the server only if the previous update's age and value difference surpasses a certain threshold that depends on the specifics of the value function and system statistics.