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REBATE: A REpulsive-BAsed Traffic Engineering protocol for dynamic scale-free networks

机译:回扣:用于动态无标度网络的排斥基础的流量工程协议

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Nowadays the abundance of IoT devices has the potential of changing our lives dramatically, but brings new routing and traffic orchestration challenges for the next-generation Internet providers: core routers are already overwhelmed, see e.g, the routing table size growth problem. Although some researchers still argue whether or not the next-generation networks should feature scale-free properties, recent results have shown benefits of embedding such scale-free networks in a hyperbolic space of negative curvature. Specifically, this allows geometrically route packets by using only a local topology knowledge (i.e., with average O*(1) space-time complexity) at no extra communication overhead (i.e., without routing protocols). To our knowledge, however, there is no Traffic Engineering (TE) protocol with the aforementioned properties that can be used in dynamic scale-free networks. In this paper, we propose the first to our knowledge REpulsive-BAsed Traffic Engineering (REBATE) protocol for dynamic scale-free networks. REBATE is built upon dual principles of the demand-aware TE and fundamentals properties of hyperbolic spaces. Using trace-driven numerical simulations, we then show how REBATE can reduce the maximum link utilization up to 25% when compared to a common geometric routing-based traffic steering. Although REBATE can perform worse than common demands-aware and oblivious TE approaches, we think that our work should pave the way for more efficient TE in the next-generation dynamic scale-free networks.
机译:如今,大量的IoT设备有可能极大地改变我们的生活,但给下一代Internet提供商带来新的路由和流量编排挑战:核心路由器已经不堪重负,例如,路由表规模增长问题。尽管一些研究人员仍在争论下一代网络是否应具有无标度特性,但最近的研究结果表明,将此类无标度网络嵌入到负曲率的双曲空间中的好处。具体而言,这允许通过仅使用本地拓扑知识(即,具有平均O *(1)时空复杂度)来几何地路由分组,而没有额外的通信开销(即,没有路由协议)。然而,据我们所知,尚无具有上述特性的流量工程(TE)协议​​可用于动态无标度网络。在本文中,我们向动态无标度网络的知识排斥式流量工程(REBATE)协议​​提出了第一个建议。 REBATE建立在需求感知TE的双重原则和双曲空间的基本属性的基础上。然后,使用跟踪驱动的数值模拟,我们展示了与基于几何路由的常见流量转向相比,REBATE如何将最大链路利用率降低多达25%。尽管REBATE的性能可能比一般的需求感知和遗忘的TE方法差,但我们认为我们的工作应为下一代动态无标度网络中更有效的TE铺平道路。

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