Waiting But Not Aging: Optimizing Information Freshness Under the Pull Model
Waiting But Not Aging: Optimizing Information Freshness Under the Pull Model
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DOI：
10.1109/tnet.2020.3041654
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发表时间：
2021-02-01
影响因子：
3.7
通讯作者：
Ji, Bo
中科院分区：
文献类型：
--
作者：
Li, Fengjiao;Sang, Yu;Ji, Bo
信息量是衡量信息更新系统及时性的重要指标。在本文中，我们研究了AoI最小化问题下的一个新的拉模型与复制方案，其中用户主动发送一个复制的请求到多个服务器“拉”感兴趣的信息。有趣的是，我们发现，在这个新的拉模型下，复制方案捕获一个新的权衡不同的价值观的AoI跨服务器（由于随机更新过程）和不同的响应时间跨服务器，可以利用它来最大限度地减少预期的AoI在用户的一面。具体来说，假设泊松更新过程的服务器和指数分布的响应时间，我们推导出一个封闭形式的公式来计算预期的AoI，并获得最佳的响应数量等待，以尽量减少预期的AoI。然后，我们将我们的分析扩展到用户的目标是最大限度地提高基于AoI的实用程序，这代表了用户的满意度相对于所接收的信息的新鲜度的设置。此外，我们考虑一个更现实的情况下，用户没有系统的先验知识。在这种情况下，我们将效用最大化问题重新表述为具有侧观测的随机多臂强盗问题，并利用侧观测的特殊线性结构来设计具有改进性能保证的学习算法。最后，我们进行了广泛的模拟，阐明我们的理论结果和比较不同的算法的性能。我们的研究结果表明，在拉动模型下，等待并不一定会导致老化;在大多数情况下，等待多个响应通常可以显着降低AoI并提高基于AoI的效用。
The Age-of-Information is an important metric for investigating the timeliness performance in information-update systems. In this paper, we study the AoI minimization problem under a new Pull model with replication schemes, where a user proactively sends a replicated request to multiple servers to "pull" the information of interest. Interestingly, we find that under this new Pull model, replication schemes capture a novel tradeoff between different values of the AoI across the servers (due to the random updating processes) and different response times across the servers, which can be exploited to minimize the expected AoI at the user's side. Specifically, assuming Poisson updating process for the servers and exponentially distributed response time, we derive a closed-form formula for computing the expected AoI and obtain the optimal number of responses to wait for to minimize the expected AoI. Then, we extend our analysis to the setting where the user aims to maximize the AoI-based utility, which represents the user's satisfaction level with respect to freshness of the received information. Furthermore, we consider a more realistic scenario where the user has no prior knowledge of the system. In this case, we reformulate the utility maximization problem as a stochastic Multi-Armed Bandit problem with side observations and leverage a special linear structure of side observations to design learning algorithms with improved performance guarantees. Finally, we conduct extensive simulations to elucidate our theoretical results and compare the performance of different algorithms. Our findings reveal that under the Pull model, waiting does not necessarily lead to aging; waiting for more than one response can often significantly reduce the AoI and improve the AoI-based utility in most scenarios.