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arXiv 1704.04848cs.NI
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等待多个响应在最小化信息量方面的力量

The Power of Waiting for More than One Response in Minimizing the Age-of-Information

Yu Sang, Bin Li, Bo Ji

摘要:

最近提出的信息的时效性(AoI)作为一个重要的指标,调查在信息更新系统的及时性性能。关于AoI优化的先前研究通常考虑推模型,其关注何时以及如何“推”(即,生成并发送)更新的信息给用户。与此形成鲜明对比的是,在本文中,我们引入了一个新的拉模型,这是更相关的某些应用程序(如实时股票报价服务),其中用户发送请求到服务器主动“拉”感兴趣的信息。此外,我们建议采用请求复制来减少AoI。有趣的是,我们发现,在这个新的拉模型,复制方案捕获一个新的权衡不同级别的信息新鲜度和不同的响应时间之间的服务器,可以利用它来尽量减少预期的AOI在用户的一面。具体来说,假设泊松更新过程中的服务器和指数分布的响应时间,我们推导出一个封闭的公式计算预期的AoI和获得的最佳数量的响应等待,以尽量减少预期的AoI。最后,我们进行数值模拟来阐明我们的理论结果。我们的研究结果表明,在大多数情况下,等待多个响应可以显着降低AoI。

英文摘要:

The Age-of-Information (AoI) has recently been proposed as an important metric for investigating the timeliness performance in information-update systems. Prior studies on AoI optimization often consider a Push model, which is concerned about when and how to "push" (i.e., generate and transmit) the updated information to the user. In stark contrast, in this paper we introduce a new Pull model, which is more relevant for certain applications (such as the real-time stock quotes service), where a user sends requests to the servers to proactively "pull" the information of interest. Moreover, we propose to employ request replication to reduce the AoI. Interestingly, we find that under this new Pull model, replication schemes capture a novel tradeoff between different levels of information freshness 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 at the servers and exponentially distributed response time, we derive a closedform formula for computing the expected AoI and obtain the optimal number of responses to wait for to minimize the expected AoI. Finally, we conduct numerical simulations to elucidate our theoretical results. Our findings show that waiting for more than one response can significantly reduce the AoI in most scenarios.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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