These days, networks aren't just racks of switches anymore, they're also AI clusters. A single training job depends on a lossless fabric, a Kubernetes control plane, a cloud VPC, an inference gateway, and a telemetry pipeline that has to notice trouble before anybody files a ticket. Even with something as strong as Python glue, it's not going to hold up at that size. But C++23 doesn't. When you get to the end of this book, you'll have a fully-fledged automation tool at your disposal.
The book kicks off with std::expected, std::span, ranges, and coroutines applied to real device work, then builds a CMake and vcpkg project with sanitizers, tests, and a containerlab fabric. Then you can drive NETCONF, YANG, and gNMI, provision servers over SSH and Ansible, program OVSDB and VXLAN overlays, automate Suricata and Vectorscan detection, and reach AWS and Azure through one interface. The last couple of chapters show you how to use Kubernetes resources on the server side, attach pods to RDMA devices through Multus and SR-IOV, and validate PFC and ECN across a RoCEv2 fabric. Plus, there'll be token streaming for vLLM replicas, your tooling will be exposed as an MCP server, and the loop will be closed with OpenTelemetry and guarded remediation. Everything's on one Linux laptop.
If you're a network engineer, systems programmer, or part of an infrastructure team, then this book is for you. You don't need to have any prior experience with SDN, Kubernetes, or machine learning for this book. Just some basics of coding around C++ and fundamentals of networking is sufficient.
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