Autonomous Control Loops for Self-Healing Internet Backbone Architectures

The Internet backbone's scale and complexity make maintaining seamless service during failures,

congestion, and misconfigurations highly challenging. Traditional fault management techniques that

rely on manual work, automated switch-over systems, or static failover systems, are not adaptive

enough to 'hot' recovery. This paper proposes an innovative architecture that focuses on integrating

autonomous control loops to the Internet backbone for the purpose of real-time self-healing. Such

systems are inspired by the principles of autonomic computing and Monitor–Analyse–Decide–Act

(MADA) model and employ intelligent telemetry collection, ML-based anomaly and fault detection,

and automated systems for self-healing. The control loops act on the routing and transport layers,

which allows embedding key decisions on the distributed backbone nodes that need to collaborate

for the global objective. The architecture allows for early fault detection through the utilization of

predictive analytics and root cause analysis through graph reasoning, both reinforced by looped

feedback systems that adapt to network condition changes. Testing in a simulated ISP-scale network

showed mean time to recovery (MTTR) reduced by 45%, and static redundancy-based methods

showed 60% improvement in precision of anomaly detection. Autonomous self-healing Internet

infrastructure systems can better adapt and respond to crises. This research forms the basis for more advanced fault-tolerant backbone architectures for next-generation systems, as well as adaptive, intelligent, and autonomous systems that require little human input to self-optimize continuously.