AI-Driven ITSM DevOps Integration for Network Change Automation
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Solution Overview
Problem
The existing integration of Information Technology Service Management (ITSM) and Development Operations (DevOps) methodologies often fail to work in harmony, leading to extended time-to-market cycles and potential service instability due to their differing approaches, with ITSM's cautious methods resulting in longer deployment times and DevOps' agility causing potential instability.
Innovation Solution
A tool leveraging artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA) is developed to integrate DevOps and ITSM, providing end-to-end automation for network infrastructure development changes, anticipating disruptions, managing incidents, and monitoring performance to enhance productivity and service reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ITSM's cautious and methodical approach is used, then service reliability is improved, but time-to-market is extended
Solution Approach 1:
The system performs preliminary actions by automatically reviewing change requests against historical data and AI predictions before deployment. The AI model analyzes past incidents and changes to predict potential failures, allowing the system to prepare mitigation strategies in advance and perform risk assessments before actual deployment occurs, thus maintaining reliability while reducing manual review time
Solution Approach 2:
The system implements continuous feedback loops where performance data from production environments is automatically collected and fed back into the AI model. This feedback mechanism allows the system to learn from actual outcomes, refine its predictions, and adjust future change recommendations, creating a self-improving system that maintains high reliability through data-driven decisions while accelerating deployment cycles
2Loss of time
If DevOps' agile approach is used, then time-to-market is reduced, but service stability deteriorates
Solution Approach 1:
The system enables self-service automation where the AI model autonomously evaluates change requests, predicts potential issues, and recommends actions without requiring manual intervention. The system automatically creates change requests, performs risk assessments, and coordinates deployments, allowing DevOps teams to maintain agile speeds while the AI ensures stability through continuous automated monitoring and prediction
Solution Approach 2:
The patent replaces manual mechanical review processes with AI-based automated systems. Instead of human reviewers manually checking each change request, the AI model automatically analyzes change impact, predicts failures based on historical data, and generates deployment recommendations, substituting human judgment with intelligent automation that maintains both speed and stability
3Device complexity
If manual integration between ITSM and DevOps is used, then process complexity is reduced, but productivity decreases
Solution Approach 1:
The system merges ITSM and DevOps processes into a unified automated workflow. The AI model integrates change management, incident response, and deployment operations into a single coordinated system that automatically handles the entire lifecycle of changes, eliminating the need for separate manual processes and improving productivity through streamlined operations
Solution Approach 2:
The AI model serves multiple functions simultaneously - it analyzes change requests, predicts failures, generates mitigation strategies, coordinates deployments, and monitors production performance. This multi-functional approach consolidates what would otherwise require multiple separate systems and manual processes, reducing overall complexity while increasing productivity through automation
Data Source
AI summary
Systems, computer program products, and methods are described herein for end-to-end automation of network infrastructure development changes. The present disclosure is configured to collect and archive data comprising feature logs, resource logs, and events in a central historical data storage. This data is subsequently normalized and events are correlated to specific features. This correlation involves locating differences in pre and post-deployment feature performance, which includes resource utilization, and maintaining a history of the analyzed data in the central historical data storage.