我应该播种还是不应该播种:关于在D2D卸载中播种者的报酬

Filippo Rebecchi, M. Amorim, V. Conan
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引用次数: 10

摘要

利用机会性设备对设备(D2D)通信进行流量分流是蜂窝运营商应对前所未有的移动数据增长的一个令人兴奋的新机遇。现有建议的一个限制是,它们假设所有终端在默认情况下都参与D2D转发过程。特别是,它们没有捕捉到奖励种子用户的需求。出于这个原因,我们在机会主义卸载策略的设计中包含了奖励成本。在我们的解决方案中,我们区分了只通过蜂窝通道接收内容的节点(leechers)和参与转发过程的节点(seeders)。运营商的关键是设计全局策略,选择哪些节点作为播种者,哪些节点作为索取者,以降低总传播成本。我们把这个问题表示成一个随机控制问题,并应用庞特里亚金的极大值原理来解决这个问题。我们提供了一个数学框架,在一般成本模型下设计机会性卸载的最佳策略。首先,我们证明了存在一个最优解;然后,从这一政策中,我们提取了一些见解来开发启发式。最后,我们讨论了该模型相对于经典的纯种子模型的优点。我们证明,在具有大交付延迟跨度的最苛刻的内容情况下,分离种子/leecher导致更好的激励策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Should I seed or should I not: On the remuneration of seeders in D2D offloading
Traffic offloading using opportunistic device-to-device (D2D) communications is a new and exciting opportunity for cellular operators to cope with the unprecedented mobile data growth. A limitation of existing proposals is that they assume that all terminals are, by default, involved in the D2D forwarding process. In particular, they do not capture the need to reward seed users. For this reason, we include a rewarding cost in the design of the opportunistic offloading strategy. In our solution, we make the difference between nodes that receive content through the cellular channel only (leechers) and nodes that take part in the forwarding process (seeders). The key point for an operator is to design a global strategy to select which nodes act as seeders and which ones as leechers, in order to reduce the total dissemination cost. We formulate this question as a stochastic control problem that we solve using an application of Pontryagin's Maximum Principle. We provide a mathematical framework to devise the optimal strategy for opportunistic offloading under a generic cost model. First, we show that an optimal solution exists; then, from this policy, we extract some insights to develop heuristics. Finally, we discuss the advantages of the proposed model compared to the classic seeder-only model. We demonstrate that separating seeders/leechers leads to better incentive strategies in the most demanding cases of content with a large span of delivery delays.
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