Live streaming services power mainstream real-time interactions on top of dedicated live streaming networks (LiveNets). Yet making LiveNets reliable at scale is challenging: failures arise on the userfacing delivery path and within streaming protocol and application logic, so operators need both continuous runtime monitoring to detect and localize incidents quickly and proactive preflight testing to exercise changes under representative environments and sustained playback behavior. Meeting these goals hinges on the right vantage point: the observability workflow must traverse the same network paths and delivery stacks as users while remaining controllable and non-intrusive. We present Horizon, which leverages near-user, provider-managed hyper-edge devices and orchestrates them into a shared fleet that supports both always-on monitoring and customizable, scenario-driven validation. Horizon has been deployed in production for over three years; in 2025, it identified 2,000+ major network incidents using 100,000+ hyper-edge agents.

As the number of data center servers grows into the millions and due to the demand for more accurate, rapid and powerful network fault detection and location, the existing Pingmesh-centric monitoring and diagnostic system is not efficient enough. In this paper, we propose ByteTracker, the first agentless probing and diagnostic system for large-scale data center networks. It does not need to deploy probe processes or make any configurations on end hosts, and all probes are launched by a small number of centralized Probers. ByteTracker achieves accurate, real-time probe path tracking with packet mirroring on switches. By reducing end-host probe noise, precisely identifying network timeout probes, accurately tracking probe paths, and marking the failed switch with multiple network timeout probes, ByteTracker can locate network failures with nearly 100% accuracy. We have deployed ByteTracker in all of our data centers for over half a year. During deployment, ByteTracker can detect almost all network anomalies and locate them within 5 seconds with 100% accuracy.

RoCE services are sensitive to failures and bottlenecks, which become more common as the RoCE network scales. To effectively detect and locate these problems independent of service traffic, RoCE networks require a monitoring and diagnostic system based on active probing. However, existing active probing schemes typically rely on a controller to design the probing plan for each server, which is difficult to deploy and has high synchronization overhead in multi-tenant clusters. Fortunately, rail-optimized clusters have become more common in recent years to improve network performance. In these clusters, the controller is unnecessary.

RoCE services are sensitive to network failures and performance bottlenecks, which become more common as the RoCE network scales. In addition, some non-network problems behave like network problems and can waste troubleshooting time. However, existing mechanisms cannot quickly detect and locate network problems or determine whether the service problem is network-related.