Autonomous Test Fabric Architectures: Deep Reinforcement Learning For Hyperscale Network Infrastructure Validation

Abstract

Hyperscale data center infrastructures today require validation methodologies to keep up with the rate of increase in the complexity of the network. Manual test design methods and other traditional regression frameworks generate severe disjunctions between the pace of infrastructure evolution and maturation in validation ability. Autonomous Test Fabric Architecture: This is a paradigm shift to self learning validation ecosystems based on deep reinforcement learning, causal inference, and digital twin technologies. The framework applies the multi-layer system design that includes data ingestion, learning engines, orchestration, execution, and continuous feedback. Deep reinforcement learning agents independently grammatically construct and prioritise test situations and trade off exploration of novel fault modes and exploitation of strategies whose validity has been confirmed. To provide predictive fault discovery, causal knowledge graphs are used to correlate topology attributes, performance parameters, and failure phenomena. Digital twin systems are enabled to create dynamic testbeds that can cope with any arbitrary network design without jeopardizing production stability. The coordination of multi-agents spreads the validation to specialist areas, and the real-time integration of telemetry facilitates the adaptable execution of tests. The autonomous architecture solves the key constraints, such as the inability to cover static resources, reactive errors, and wasteful use of resources. It can be used in network infrastructure validation, cloud deployment validation, interconnect fabrics of artificial intelligence, and dynamic topology environments that need constantly changing and smart resource allocation.