数据网的主动数据下载和用户需求塑造
Proactive Data Download and User Demand Shaping for Data Networks
摘要：
在这项工作中，我们提出并研究了最佳的主动资源分配和需求塑造的数据网。受最近关于数据网中人类行为模式的可预测性的研究结果以及高性能手持设备的出现的启发，我们的设计旨在随着时间的推移平滑网络流量并最小化数据传输成本。我们的框架利用主动数据服务以及智能内容推荐方案来塑造需求。主动数据服务基于对每个用户的需求概况的统计预测在非高峰时段发生，而智能内容推荐将修改的估值分配给数据项，以便使用户的需求不那么不确定。因此，我们的建议方案的目的是提高性能的主动服务的用户需求的灵活性。我们进行理论性能分析，量化的杠杆成本降低，通过建议的框架。我们表明，成本降低规模与用户数量的成本函数规模相同的速度。此外，我们证明了\emph{demand shaping}通过智能推荐严格降低了所产生的成本，甚至低于主动下载没有建议。
英文摘要：
In this work, we propose and study optimal proactive resource allocation and demand shaping for data networks. Motivated by the recent findings on the predictability of human behavior patterns in data networks, and the emergence of highly capable handheld devices, our design aims to smooth out the network traffic over time and minimize the data delivery costs. Our framework utilizes proactive data services as well as smart content recommendation schemes for shaping the demand. Proactive data services take place during the off-peak hours based on a statistical prediction of a demand profile for each user, whereas smart content recommendation assigns modified valuations to data items so as to render the users' demand less uncertain. Hence, our recommendation scheme aims to boost the performance of proactive services within the allowed flexibility of user requirements. We conduct theoretical performance analysis that quantifies the leveraged cost reduction through the proposed framework. We show that the cost reduction scales at the same rate as the cost function scales with the number of users. Further, we prove that \emph{demand shaping} through smart recommendation strictly reduces the incurred cost even below that of proactive downloads without recommendation.
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