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Low-margin optical networking at cloud scale [Invited]

Mark Filer, Jamie Gaudette, Yawei Yin, Denizcan Billor, Zahra Bakhtiari, and Jeffrey L. Cox

Open Access Open Access

Abstract

Every day, customers across the globe connect to cloud service provider servers with requests for diverse types of data, requiring instantaneous response times and seamless availability. The physical infrastructure which underpins those services is based on optics and optical networks, with the focus of this paper being on Microsoft’s approach to the optical network. Maintaining a global optical networking infrastructure which meets these customer needs means Microsoft must utilize solutions which are highly tailored and optimized for the application space which they address, with appropriately streamlined solutions for metropolitan data center interconnect and long-haul portions of the network. This paper presents Microsoft’s approach for tackling these challenges at cloud scale, highlighting the low-margin solutions which are employed. We provide a survey of Microsoft’s regional network design and corresponding optical network architectures, and present volumes of real-time polled metrics from the thousands of lines systems and tens of thousands of transceivers deployed today. We close by describing our approach to a unified software-defined networking toolset which ultimately enables the velocity and scale with which we can grow and operate this critical network infrastructure.

© 2019 Optical Society of America

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Figures (18)
Fig. 1.
Fig. 1. Microsoft global DC + WAN footprint.

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Fig. 2.
Fig. 2. (a) Legacy “mega DC” architecture and (b) current regional architecture.

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Fig. 3.
Fig. 3. Impact of regional architecture on Microsoft deployed 100G DWDM ports.

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Fig. 4.
Fig. 4. PAM4 DCI OLS line system “stamp.”

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Fig. 5.
Fig. 5. Open line system concept.

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Fig. 6.
Fig. 6. Long-haul OLS line system “stamps.”

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Fig. 7.
Fig. 7. Long-haul OLS optical spectrum, showing fully filled C-band with data-carrying signals in the upper frequencies and ASE noise-loading throughout the remainder.

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Fig. 8.
Fig. 8. DCI fiber quality statistics.

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Fig. 9.
Fig. 9. (a) Fiber distance and (b) loss distributions of the first $ {\gt}1500$ deployed DWDM PAM4 line systems.

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Fig. 10.
Fig. 10. (a) Fiber distance, (b) loss, (c) type, and (d) route length distributions across the first 26,000 km of deployed Microsoft long-haul OLS installations. Fiber types: “LC” = large core (large effective area, e.g., $ {\ge}80\,\,{\unicode{x00B5} }{{\rm{m}}^2}$ ), “SC” = small core (small effective area, e.g.,  $ {\le}55\,\,{\unicode{x00B5}} {{\rm{m}}^2}$ ).

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Fig. 11.
Fig. 11. $Q$ -factor distribution of the first 50,000 deployed DWDM PAM4 ports.

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Fig. 12.
Fig. 12. BER stability over a 50-day period.

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Fig. 13.
Fig. 13. Temperature distribution over optical module infrastructure.

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Fig. 14.
Fig. 14. Failure rates of deployed 100G technologies.

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Fig. 15.
Fig. 15. Long-haul OLS BER statistics across 26,000 km of deployed infrastructure: (a) 16QAM, (b) 8QAM, and (c) QPSK.

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Fig. 16.
Fig. 16. Typical deployment workflow screenshot.

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Fig. 17.
Fig. 17. Screenshot taken from production tooling showing the workflow for the router linecard refresh process; “wanetmon” is an internal device health checker, “swan” is a specific WAN router variant.

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Fig. 18.
Fig. 18. Production tooling screenshot: YAML underpinning of the linecard deployment workflow shown in Fig. 17.

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Tables (1)

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Table 1. Global Fiber Specifications

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