Preemption Revisited: Multi-Threshold Preemption Policies for AoI Minimization

2026-05-15

AI Summary

This work addresses the problem of minimizing the Age of Information (AoI) in scenarios with randomly arriving updates by proposing a multi-threshold preemption policy that jointly considers packet age and system age in its decision-making. By formulating a stochastic process model that integrates both age metrics into a unified multi-threshold framework, the proposed approach overcomes the limitations of conventional single-threshold or probabilistic preemption strategies. Theoretical analysis reveals structural properties of the optimal policy, and experimental results demonstrate that the proposed method significantly outperforms existing approaches, effectively reducing AoI in stochastic update environments.

Abstract

The study of optimal preemption policies for status update systems has been a recurring topic in the age of information (AoI) literature, where threshold-based structures have been shown to be optimal under a generate-at-will update generation model under certain assumptions. In this work, we study the effectiveness of threshold-based policies for a system with random update arrivals. In this regard, we introduce an analytical framework for evaluating the AoI of multi-threshold preemption policies and present interesting characteristics of the structure of the optimal preemption policy. We show the effectiveness of these threshold-based policies over the traditional probabilistic preemption policies and single-threshold policies, where we observe that significant gains in terms of AoI can be obtained by utilizing both the age of the packet and the age of the system when designing these preemption policies.

Problem

• Age of Information
• preemption policy
• threshold-based policy
• random arrivals
• status update system

Innovation

• multi-threshold preemption
• age of information
• random update arrivals
• preemption policy
• status update systems