在小世界DTN中通过细粒度路径规划实现吞吐量
Achieving Throughput via Fine-Grained Path Planning in Small World DTNs
摘要：
我们探讨了在小世界DTN中使用细粒度统计数据的好处，以实现高吞吐量，而无需外部基础设施的帮助。我们首先设计了一个经验的节点对相互接触的模型，预测会议在一个时间框架内的适当长度，通常是几天的顺序，概率高于一些阈值，可以很容易地计算与低开销。这种时间知识使得有效的时间相关的路径规划，甚至可以响应每个数据包的最后期限的变化。我们描述了一个这样的路由框架，收割机（可靠，高效和预测路由），这是完全分布式和自我稳定。它的主要目标是提供概率边界的路径长度（成本）和延迟在时间上细粒度的方式，同时利用小世界结构，只需要多对数存储和控制开销。基于模拟的评估证实，REAPER在超轻型到重型网络流量的范围内实现了高吞吐量和能源效率，并且大大优于最先进的单副本协议以及依赖于基本上粗粒度指标的基于社交性的协议。
英文摘要：
We explore the benefits of using fine-grained statistics in small world DTNs to achieve high throughput without the aid of external infrastructure. We first design an empirical node-pair inter-contacts model that predicts meetings within a time frame of suitable length, typically of the order of days, with a probability above some threshold, and can be readily computed with low overhead. This temporal knowledge enables effective time-dependent path planning that can be respond to even per-packet deadline variabilities. We describe one such routing framework, REAPER (for Reliable, Efficient and Predictive Routing), that is fully distributed and self-stabilizing. Its key objective is to provide probabilistic bounds on path length (cost) and delay in a temporally fine-grained way, while exploiting the small world structure to entail only polylogarithmic storage and control overhead. A simulation-based evaluation confirms that REAPER achieves high throughput and energy efficiency across the spectrum of ultra-light to heavy network traffic, and substantially outperforms state-of-the-art single copy protocols as well as sociability-based protocols that rely on essentially coarse-grained metrics.
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。
