Aug 2026· Proceedings of the ACM SIGCOMM 2026 Conference· 0 citations· 28 references
TL;DR
Google's Global Network (GGN), a major architectural redesign of the WAN that evolves B2 and B4 into a single, modular, and highly available software-defined network, is presented.
Abstract
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.
The massive compute, memory, and networking needs for LLM training necessitate a fundamental rethinking of datacenter architectures to ensure scalability, efficiency, and cost-effectiveness. In particular, the design of the network fabric for AI datacenters for emerging LLMs (such as MoEs) remains a crucial and challenging open question, spanning technology choices (that determine the size of the high-bandwidth domain), topology, and software optimizations (collective algorithms and overlap strategies). This necessitates an agile framework to traverse the co-design space. This work introduces Calculon-MoE, a tool that jointly explores FLOPS, HBM bandwidth and capacity, multiple network topologies (Two-tiered vs. FullFlat optical), the size of scale-up domain, and popular parallelism/optimization strategies used in LLMs. Our validation studies demonstrate that our LLM/MoE runtime predictions are within 10% of real-world measurements. Using Calculon-MoE, we conduct a suite of case studies to develop an actionable roadmap for data centers. For example, the results point to the promise of Fullflat network architectures, which provide uniform high-bandwidth, low-latency connectivity between all nodes and demonstrate their positive impacts on performance and scalability. We also quantify the benefits of overlapping compute and communication, hardware-accelerated collectives, widening the scale-up domain, and higher memory bandwidth and capacity. Our study spans both sparse (mixture of experts) and dense transformer-based LLMs, revealing how system design and optimization choices affect system efficiency and overall throughput in both cases.
Jesmin Jahan Tithi, Hanjiang Wu, Joongun Park et al.· International Conference on...· 0 citations
Aim: This study aimed to extend the staged-rollout risk framework proposed by Zhao et al. (2018) to multi-datacenter environments by accounting for failure-domain topology, per-datacenter sequential monitoring, and automated rollback. The resulting framework, termed the Isolation Deployment Protocol (IDP), was designed to address the limitations of using traffic-percentage exposure as a proxy for failure exposure in distributed systems with shared control-plane services, replicated caches, and cross-region service-discovery dependencies.
Methods: The Isolation Deployment Protocol (IDP) was developed by integrating four components: a real-time Failure Domain Graph Model, a Topology-Aware Ramp-Up (TARU) algorithm, per-datacenter Sequential Probability Ratio Test (SPRT) monitoring, and automated topology-aware rollback. IDP was evaluated on a six-node bare-metal Kubernetes testbed running Istio and Consul, with namespace isolation used to represent separate datacenters. Twenty independent trials were conducted against a topology-blind baseline, measuring realized blast radius and Mean Time to Containment (MTTC). The blast-radius model was also validated through formal analytical analysis.
Results: Formal analysis demonstrated that the topology-blind baseline could underestimate blast radius by up to 8.62× in the evaluated failure scenario. Across 20 independent trials, IDP reduced the mean realized blast radius by a factor of 13.0 compared with the topology-blind baseline, restricting error propagation to the targeted 5% canary namespace. IDP also reduced Mean Time to Containment by 94.4%, from 142.4 minutes under global metric aggregation to 7.9 minutes with per-datacenter monitoring. These findings demonstrate that deployment decisions based on structural failure domains can substantially limit the propagation of failures beyond the intended canary population.
Conclusion: These results indicate that topology-aware rollout control and per-datacenter monitoring can substantially reduce failure propagation and containment time relative to topology-blind global monitoring.
Recommendation: The experiments ran within a single bare-metal host, and production multi-region network latency was not modeled; this represents an open limitation of the current evaluation. Future research should validate IDP across geographically distributed datacenters under realistic inter-region latency, packet loss, traffic variability, and production-scale dependency graphs.
Sai Joshitha Kathari· American Journal of Technolo...· 0 citations
Network emulation has become an indispensable methodology for evaluating next-generation network architectures, offering a critical balance between experimental fidelity and operational scalability. However, its effectiveness is fundamentally constrained by inefficiencies in emulating large-scale networks, particularly during virtual network construction. This bottleneck arises from mandatory serialization of virtual link instantiation and operating system (OS) kernel-level notification overheads, which collectively degrade performance by orders of magnitude on 10K-node topologies. Departure from the current practice that employs a multi-machine framework for improvements, we propose SplitNN (Split Network and Namespace), a novel single-machine network emulation paradigm that breaks the serialization constraint through multi-VM (virtual machines) partitioning, and reduces notification overheads via namespace segmentation. Extensive evaluations show that SplitNN constructs 10K-node virtual networks within 1–5 minutes on a single machine, achieving a 98.5%–99.2% reduction in construction time compared to state-of-the-art emulators. While primarily a single-machine solution, SplitNN seamlessly integrates with multi-machine deployments, complementing them by enabling cumulative gains in both scalability and efficiency.
Kaifei Peng, Yanbiao Li, Wenbin Li et al.· IEEE Transactions on Network...· 0 citations
Cloud-native architecture has been the new paradigm in enterprise application development that facilitates organization to reach unprecedented levels of scalability, agility, reliability, and operational efficiency. The dynamic business requirements, short delivery cycles of software solutions and the necessity of globally distributed services are mounting an increasing challenge on traditional monolithic systems. The concepts of microservices architecture, containerization, DevOps culture, and continuous delivery pipelines are the main principles in cloud-native systems that circumvent the limitations. This paper will be a detailed study of cloud-native architecture and its strategic role in a digital transformation process of businesses. Scalability models, distributed resource management, service orchestration, elasticity patterns and resilience techniques applied by cloud-native platforms are further detailed in the abstract. We address the transformation of enterprise application infrastructure out of on-premise legacy resource setting into service-based cloud environments properly configured to scale horizontally. The paper also investigates that the container orchestration systems such as Kubernetes make deployment, scaling, and failover operations to be declaratively automated. The purpose of service mesh, API-oriented architecture, event-based systems, policy-oriented autoscaling, and infrastructure-as-code (IaC) are examined to show how the architectural resilience and operational administration are accomplished. An approach to assess the maturity of cloud-native systems is presented based on the performance benchmarking, lifecycle automation, security compliance, and cost optimization indicators as part of a methodological framework. Also, the paper presents experimental evaluations of the response time, throughput, service resiliency, and infrastructure utilization in both traditional and cloud-native deployments. Findings indicate the application availability, frequency of deployment and scalability efficiency are very high. Lastly, such challenges as state management, data consistency, complexities in migrating, observability, and operational complexity are also addressed. The innovations that are emphasized by the best practices and future-oriented thinking include serverlesscomputing, auto-scaling that is run with AI, workloads based on WebAssembly, and automated cloud operations. All in all, the paper points to the idea that cloud-native architectures are not something the enterprises can afford to ignore on their quest towards maintaining competitive viability in the rapidly changing digital economy.
Kanya Mohammed, Naree Thongchai· International Journal of Mod...· 0 citations
This paper describes two generations of production container networks deployed for over five years. Our first-generation network, Nimitz, used VxLAN for overlay-underlay mapping, providing flexibility and scalability from 2019-2022. However, with the rise of large-scale services (e.g., AI training and inference), Nimitz hit the C100K problem: beyond O(100K) containers, address mapping overhead and complex packet processing caused significant performance degradation. To overcome this, we built NetPila, a second-generation design that uses simple, practical IPv6 addressing to remove VxLAN encapsulation and overlay-underlay mapping tables. By embedding container addresses in the 128-bit IPv6 space, NetPila integrates the container network with the physical network, reducing packet-processing complexity and improving scalability. NetPila now supports millions of containers per tenant for production AI training and inference. We present the design, lessons, and deployment results of both generations.
Jiaqi Gao, Chao Qin, Sheng Cheng et al.· Proceedings of the ACM SIGCO...· 0 citations
A comprehensive review of Kubernetes scheduling strategies published between January 2023 and January 2026 is presented and a multi-dimensional taxonomy is established that categorizes scheduling approaches based on common objectives, modification methods, optimization methodologies, targeted workloads, evaluation methods, scheduling scopes, and performance metrics.
Mohammed Alhakimi, R. Latip· Computers· 0 citations