AoI-Aware Markov Decision Policies for Caching

TL;DR

This paper proposes an AoI-aware Markov Decision Policy for caching in vehicular networks, balancing content freshness and system costs using MDP and Lyapunov optimization.

Contribution

It introduces a novel algorithm that optimizes cache update policies considering AoI and costs in vehicular networks.

Findings

The proposed policy reduces system costs while maintaining acceptable AoI levels.

Simulation results demonstrate improved efficiency over traditional caching strategies.

The approach effectively balances content freshness and resource utilization.

Abstract

We consider a scenario that utilizes road side units (RSUs) as distributed caches in connected vehicular networks. The goal of the use of caches in our scenario is for rapidly providing contents to connected vehicles under various traffic conditions. During this operation, due to the rapidly changed road environment and user mobility, the concept of age-of-information (AoI) is considered for (1) updating the cached information as well as (2) maintaining the freshness of cached information. The frequent updates of cached information maintain the freshness of the information at the expense of network resources. Here, the frequent updates increase the number of data transmissions between RSUs and MBS; and thus, it increases system costs, consequently. Therefore, the tradeoff exists between the AoI of cached information and the system costs. Based on this observation, the proposed algorithm in this paper aims at the system cost reduction which is fundamentally required for content delivery while minimizing the content AoI, based on Markov Decision Process (MDP) and Lyapunov optimization.