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.