提出TPAoI度量网络边缘服务状态新鲜度，优化更新策略。
TPAoI: Ensuring Fresh Service Status at the Network Edge in Compute-First Networking
- 构建马尔可夫决策过程模型，用D3QN算法动态优化更新决策。
- 相比QAoI降低47%的年龄，更新频率减少48%。
- 适合需要低延迟、高可靠性的边缘计算应用场景。
在计算优先网络中，保持网络边缘的服务状态新鲜准确对远程服务访问至关重要。该过程通常包括状态更新、用户访问和用户请求三个阶段。然而，现有状态有效性研究如查询时龄（QAoI）未能全面覆盖所有阶段。为此，本文提出一种新度量指标TPAoI，旨在通过衡量服务状态的新鲜度来优化更新决策。由于边缘环境具有通信延迟不可预测等随机特性，建模难度大。为此，将问题建模为马尔可夫决策过程（MDP），并采用双人双深度Q网络（D3QN）算法进行优化。大量实验表明，所提出的TPAoI度量能有效降低时龄，在动态边缘环境中确保及时可靠的更新。结果表明，与QAoI相比，TPAoI平均降低47%的时龄，相较于传统时龄度量，更新频率平均减少48%，性能显著提升。
原文摘要 · Abstract (English)
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.
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