Meta's rapid expansion in users, business operations, and AI workloads is straining our backbone network, while physical constraints—such as fiber, space, and power—limit the speed of capacity growth. To address these challenges, we present a service-aware network capacity planning suite that systematically improves network efficiency with a service collaboration approach. We propose the "safe capacity" abstraction which enables services to incorporate current and projected network conditions into their compute and storage allocation decisions. We introduce a hose-carving method that efficiently translates service-level traffic demands into detailed traffic matrices, allowing for more precise bandwidth allocation. To promote responsible network usage, we design a network rate card which attributes network consumption to individual services, incentivizing optimization and resource trade-offs. Additionally, new enforcement features at the end-host layer dynamically adjust resource allocations and traffic flows at runtime to maximize utilization. This paper is the first to detail a collaborative, service-aware approach to backbone network efficiency at Meta scale. Based on years of operational experience, we share practical insights and highlight new directions for research in network efficiency.

Advancements in Large Language Models (LLMs) are significantly transforming network management practices. In this paper, we present our experience developing Confucius, a multi-agent framework for network management at Meta. We model network management workflows as directed acyclic graphs (DAGs) to aid planning. Our framework integrates LLMs with existing management tools to achieve seamless operational integration, employs retrieval-augmented generation (RAG) to improve long-term memory, and establishes a set of primitives to systematically support human/model interaction. To ensure the accuracy of critical network operations, Confucius closely integrates with existing network validation methods and incorporates its own validation framework to prevent regressions. Remarkably, Confucius is a production-ready LLM development framework that has been operational for two years, with over 60 applications onboarded. To our knowledge, this is the first report on employing multi-agent LLMs for hyper-scale networks.

This paper presents Meta's Production Wide Area Network (WAN) Entitlement solution used by thousands of Meta's services to share the network safely and efficiently. We first introduce the Network Entitlement problem, i.e., how to share WAN bandwidth across services with flexibility and SLO guarantees. We present a new abstraction entitlement contract, which is stable, simple, and operationally friendly. The contract defines services' network quota and is set up between the network team and services teams to govern their obligations. Our framework includes two key parts: (1) an entitlement granting system that establishes an agile contract while achieving network efficiency and meeting long-term SLO guarantees, and (2) a large-scale distributed run-time enforcement system that enforces the contract on the production traffic. We demonstrate its effectiveness through extensive simulations and real-world end-to-end tests. The system has been deployed and operated for over two years in production. We hope that our years of experience provide a new angle to viewing WAN network sharing in production and will inspire follow-up research.