Optimizing the Age of Information with Segmentation and Predictive Scheduling
Optimizing the Age of Information with Segmentation and Predictive Scheduling
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DOI：
10.1109/wcnc55385.2023.10118997
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发表时间：
2023-03
期刊：
影响因子：
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通讯作者：
Jin Zhang;P. Zou;Suresh Subramaniam
中科院分区：
文献类型：
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作者：
Jin Zhang;P. Zou;Suresh Subramaniam
信息时代（AoI）是一个经过充分研究的从传感器收集数据的及时性指标。为了优化AoI，已经提出了各种分组调度策略。这是自然的问题，这些调度策略可以改善多少使用预测和多少预测数据包是必要的，以达到最佳。给定分组序列，必须存在至少一个分组组合以实现最优AoI，这可以通过穷尽保留/拒绝分组的所有可能决策来获得。然而，这是不切实际的，以获得每个更新的到达和服务时间的先验知识。此外，这种详尽的调度策略消耗了无法负担的时间，能源和计算能力的资源，在本文中，我们表明，一个足够长的数据包序列可以被分割成局部历元不变的全局最优策略，和每个历元的局部优化增量聚合的全局最优AOI的整个序列。这个新的视角还解释了空闲等待时间超过立即传输更新的反直觉现象[1]-[3]。通过与其他调度策略的AoI比较，我们发现预测调度的优化性能优于其他调度策略。
Age of Information (AoI) is a well-investigated timeliness metric for data collected from sensors. Various packet scheduling policies have been proposed in order to optimize AoI. It is natural to raise the question of how much these scheduling policies could be improved using prediction and how many predictive packets are necessary to achieve the optimum. Given a packet sequence, there must be at least one packet combination to achieve the optimal AoI, which can be obtained by exhausting all possible decisions of preserving/rejecting packets. However, it is impractical to obtain prior knowledge of each update’s arrival and service time. In addition, this exhaustive scheduling policy consumes unaffordable resources of time, energy, and computing power.In this paper, we show that a sufficiently long packet sequence may be segmented into local epochs with invariant global optimal policy, and local optimization of each epoch incrementally aggregates the global optimal AoI of the entire sequence. This new perspective also explains the counter-intuitive phenomenon [1]–[3] of idle waiting time outperforming transmitting updates immediately. After comparing with the AoIs obtained from other scheduling policies, we find that the optimization performance of predictive scheduling prevails over others.