# TPAoI: Ensuring Fresh Service Status at the Network Edge in Compute-First Networking

arXiv: https://arxiv.org/abs/2412.18391 · HTML: https://mlsanity.com/paper/2412.18391/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 32 references that are in the map.
- First posted on arXiv 24 Dec 2024.

## Builds on (most cited first, 2 of 2)

- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Computing Power Network: A Survey](https://mlsanity.com/paper/2210.06080/) (2022)

## Built on by (most cited first, 1 of 1)

- [Efficient Information Updates in Compute-First Networking via Reinforcement Learning with Joint AoI and VoI](https://mlsanity.com/paper/2505.06025/) (2025)

## Abstract (arXiv)

In compute-first networking, maintaining fresh and accurate status information at the network edge is crucial for effective access to remote services. This process typically involves three phases: Status updating, user accessing, and user requesting. However, current studies on status effectiveness, such as Age of Information at Query (QAoI), do not comprehensively cover all these phases. Therefore, this paper introduces a novel metric, TPAoI, aimed at optimizing update decisions by measuring the freshness of service status. The stochastic nature of edge environments, characterized by unpredictable communication delays in updating, requesting, and user access times, poses a significant challenge when modeling. To address this, we model the problem as a Markov Decision Process (MDP) and employ a Dueling Double Deep Q-Network (D3QN) algorithm for optimization. Extensive experiments demonstrate that the proposed TPAoI metric effectively minimizes AoI, ensuring timely and reliable service updates in dynamic edge environments. Results indicate that TPAoI reduces AoI by an average of 47% compared to QAoI metrics and decreases update frequency by an average of 48% relative to conventional AoI metrics, showing significant improvement.