Overview
IAS (Intelligent Assurance Service) leverages AI analytics capabilities to achieve end-to-end fault self-healing. The system automatically triggers fault recovery processes through anomaly detection, shifting network operations from reactive maintenance to proactive automation, significantly reducing Mean Time to Repair (MTTR) and improving network availability.
Background
As 5G, gigabit networks, and cloud architectures accelerate their convergence, network environments are characterized by high complexity across multiple vendors and domains. Traditional manual operations and maintenance can no longer be sustained: alarm storms overwhelm fault root causes, fault handling remains reactive, and operational expenditure (OPEX) continues to rise. To address these challenges, the global communications industry is accelerating its evolution toward "zero-touch, closed-loop" high-level Autonomous Networks (AN).
Solution
IAS perceives network dynamics in real time through comprehensive monitoring and data quality management, diagnoses network anomalies based on AI-powered topology correlation and Root Cause Analysis (RCA), and coordinates with automated provisioning systems to achieve agile, closed-loop fault self-healing.
IAS Suite Architecture
IAS is designed to build an AI-based intelligent network assurance platform, integrating full-domain performance management, centralized fault management, and work order dispatching capabilities to comprehensively improve network operations efficiency and customer satisfaction.
Out-of-the-Box Data Collection Capabilities
Based on adapter-driven and task-driven mechanisms, IAS provides a rich set of out-of-the-box (OOTB) adapters, supporting multi-vendor, multi-technology network access, enabling automatic data collection and resource discovery, and ensuring data accuracy and real-time performance.
IAS Core Capabilities
Inspur IAS Suite combines intelligent monitoring, precise fault handling, and flexible configuration to support higher-level autonomous network capabilities.
Policy-Driven Closed-Loop Service Assurance
Aggregating multi-domain collected data and combining AI intelligent analysis capabilities, it builds an end-to-end closed-loop system to achieve zero-touch service provisioning and automatic fault self-healing, supporting efficient network operations.
AI-Empowered: Closed-Loop Fault Handling
End-to-end fault management covers key stages including fault identification, fault diagnosis, fault resolution, and recovery verification. Through AI and machine learning technologies, it significantly improves network operations efficiency and service reliability.
Customer Value
Intelligent Assurance, Cost Reduction and Efficiency Improvement
The AI engine enables precise Root Cause Analysis (RCA), reducing Mean Time to Repair (MTTR) by 40% and improving fault identification efficiency by 30%–50%.
Closed-Loop Self-Healing, Delivering Ultimate Experience
Through IAS's seamless coordination, the system can automatically trigger network reconfiguration before users perceive faults, achieving closed-loop fault self-healing.
Decoupled Agility and Zero-Code Scenario Extension
IAS adopts a fully cloud-native architecture, modular OpenAPI, and workflow-driven policy orchestration, breaking free from hard-coded constraints to achieve more agile scenario extension.
