箭头:恢复感知交通工程

Zhizhen Zhong, M. Ghobadi, Alaa Khaddaj, J. Leach, Yiting Xia, Ying Zhang
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引用次数: 20

摘要

光纤切割事件使广域网(wan)的容量减少了几个Tbps。在本文中,我们通过重新配置从切割纤维到健康纤维的波长来恢复失去的容量。我们强调了使先前的解决方案不切实际的两个挑战,并提出了一个称为Arrow的系统来解决它们。首先,我们的测量结果表明,在大多数情况下,失去的能力只能部分恢复。这从流量工程(TE)的角度提出了一个以前没有考虑过的跨层挑战:“应该恢复哪些IP链接,以及恢复多少才能最符合TE的目标?”为了应对这一挑战,Arrow的恢复感知TE系统采用一组部分恢复候选(我们称之为LotteryTickets)作为输入,并主动找到最佳恢复计划。其次,先前的工作没有考虑放大器的重构延迟。然而,在实际设置中,放大器增加了数十分钟的重新配置延迟。为了实现快速和实用的恢复,Arrow利用光学噪声负载并绕过放大器重新配置。我们使用大规模模拟和测试平台来评估Arrow。我们的测试平台演示了Arrow的端到端恢复延迟为8秒。我们的大规模模拟将Arrow与最先进的TE方案进行了比较,并表明它可以支持2.0 -2.4倍的需求,而不会影响99.99%的可用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ARROW: restoration-aware traffic engineering
Fiber cut events reduce the capacity of wide-area networks (WANs) by several Tbps. In this paper, we revive the lost capacity by reconfiguring the wavelengths from cut fibers into healthy fibers. We highlight two challenges that made prior solutions impractical and propose a system called Arrow to address them. First, our measurements show that contrary to common belief, in most cases, the lost capacity is only partially restorable. This poses a cross-layer challenge from the Traffic Engineering (TE) perspective that has not been considered before: “Which IP links should be restored and by how much to best match the TE objective?” To address this challenge, Arrow's restoration-aware TE system takes a set of partial restoration candidates (that we call LotteryTickets) as input and proactively finds the best restoration plan. Second, prior work has not considered the reconfiguration latency of amplifiers. However, in practical settings, amplifiers add tens of minutes of reconfiguration delay. To enable fast and practical restoration, Arrow leverages optical noise loading and bypasses amplifier reconfiguration altogether. We evaluate Arrow using large-scale simulations and a testbed. Our testbed demonstrates Arrow's end-to-end restoration latency is eight seconds. Our large-scale simulations compare Arrow to the state-of-the-art TE schemes and show it can support 2.0x--2.4x more demand without compromising 99.99% availability.
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