Arrow

Abstract

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 2.0×-2.4× more demand without compromising 99.99% availability.

A Deep Learning Perspective on Network Routing

“…TE has been extensively studied [5, 7, 10, 11, 14, 16, 22, 24, 26-28, 37, 39, 57, 59] in a broad variety of settings, including legacy networks [13,17], datacenter networks [6], and backbone networks [27]. SDN-controlled WAN TE has also received extensive attention [11,22,24,35,[37][38][39]59].…”