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Delay Gain Analysis of Wireless Multicasting for Content Distribution

机译:延迟增益分析内容分布的无线多播

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In this work, we provide a comprehensive analysis of stability properties and delay gains that wireless multicasting capabilities, as opposed to more traditional unicast transmissions, can provide for content distribution in mobile networks. In particular, we propose a model and characterize the average queue-length (and hence average delay) performance of unicasting and various multicasting strategies for serving a dynamic user population at the wireless edge. First, we show that optimized static randomized multicasting (we call it 'blind multicasting') leads to stable-everywhere operation irrespective of the network loading factor (given by the ratio of the demand rate to the service rate) and the content popularity distribution. In contrast, traditional unicasting suffers from unstable operation when the loading factor approaches one, although it outperforms blind multicasting at small loading factor levels. This motivates us to study 'work-conserving multicast' policies next that always outperform unicasting while still offering stable-everywhere operation. Then, in the worst-case of uniformly-distributed content popularity, we explicitly characterize the scaling of the average queue-length (and hence delay) under a first-come-first-serve multicast strategy as a function of the database size and the loading factor. Consequently, this work provides the fundamental limits, as well as the guidelines, for the design and performance analysis of efficient multicasting strategies for wireless content distribution.
机译:在这项工作中,我们提供了对稳定性的全面分析,并且无线多播功能与更传统的单播传输相反,可以提供移动网络中的内容分发。特别地,我们提出了一种模型,并表征了单播的平均队列长度(以及因此平均延迟)性能,以及用于在无线边缘在无线边缘服务的动态用户群体的各种多播策略的性能。首先,我们表明优化的静态随机多播(我们称之为“盲多播”)导致稳定 - 到处运行,而不管网络加载因子(由需求率与服务率的需求率的比率给出)和内容普及分布。相比之下,当装载因子接近一个时,传统的单播受到不稳定的操作,尽管它在小的加载因子水平下倾斜盲多播。这使我们能够研究“工作保存的多播”政策,始终超出单播,同时仍然提供稳定的运行。然后,在均匀分布的内容受欢迎程度的最坏情况下,我们明确地表征了在一个先前一致的多播策略下的平均队列长度(和因此延迟)的缩放,作为数据库大小的函数和装载因子。因此,这项工作为无线内容分发有效多播策略的设计和性能分析提供了基本限制,以及指导方针。

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