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

TL;DR

This paper introduces TPAoI, a new metric for optimizing service status freshness at network edges in compute-first networking, using a D3QN-based approach to minimize Age of Information effectively.

Contribution

It proposes TPAoI, a novel metric for status freshness, and models the optimization as an MDP solved with deep reinforcement learning, addressing stochastic edge environment challenges.

Findings

TPAoI reduces AoI by 47% compared to QAoI.

TPAoI decreases update frequency by 48% relative to conventional AoI.

The approach ensures more timely and reliable service updates in dynamic environments.

Abstract

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.