Priya Mahadevan
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GGN: Experiences in Designing and Deploying Next-Generation Google Global Network
Richard Alimi
Arda Balkanay
Nachikethas Jagadeesan
Warren Martins
Arjun Muralidharan
Namrata Pralhad Kadam
Aman Shaikh
Sankalp Singh
Charith Wickramaarachchi
Min Zhu
2026
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Cloud and AI/ML workloads are posing unprecedented new requirements on the wide-area network: it must combine strict availability, massive growth, and feature agility. It became increasingly clear that traditional WAN designs were ill-equipped to adapt to these requirements.
We present Google's Global Network (GGN), a major architectural redesign of our WAN that evolves B2 and B4 into a single, modular, and highly available software-defined network. The architecture is designed around three pillars: (1) A modular design of functional domains with well-defined APIs; (2) a physically sharded and regionalized core for fault isolation and horizontal scaling; (3) a vendor-agnostic hardware strategy based on open standards. We share the multi-year deployment journey of GGN, including a safe, host-steered migration strategy, and demonstrate its ability to improve network availability and reaction time to failures, setting a foundation for a planet-scale modern WAN.
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Data-driven Networking Research: models for academic collaboration with Industry (a Google point of view)
Christophe Diot
Computer Communication Review, 51:4 (2021), pp. 47-49
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We (Google's networking teams) would like to increase our collaborations with academic researchers related to data-driven networking research.
There are some significant constraints on our ability to directly share data, and in case not everyone in the community understands these, this document provides a brief summary.
There are some models which can work (primarily, interns and visiting scientists).
We describe some specific areas where we would welcome proposals to work within those models
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