vRAN Event Correlation Engine for Automated Application Healing
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Solution Overview
Problem
Existing event-driven architectures face challenges in efficiently processing and correlating large volumes of events across complex networks, leading to inefficiencies in identifying critical events and managing network resources effectively.
Innovation Solution
A correlation and policy engine (CPE) that aggregates, normalizes, and analyzes event data, automates workflows, and performs real-time root cause analysis, anomaly detection, and security analytics to identify relevant events and trigger appropriate actions based on predefined policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If event-driven architecture is used to process events across complex networks, then system responsiveness and automation capability are improved, but event correlation efficiency and critical event identification become deteriorated due to large volumes of events
Solution Approach 1:
The patent introduces an event correlation engine as an intermediary component between event sources and event consumers. This engine aggregates events from multiple sources, normalizes them into a common format, and applies correlation rules to identify critical events. The intermediary layer simplifies the complex task of event correlation by providing structured processing pipelines, correlation rules, and normalization capabilities, thereby resolving the contradiction between maintaining high system responsiveness and managing event correlation complexity.
2Measurement precision
If manual monitoring and analysis of events are performed, then detailed analysis capability is maintained, but time consumption and operational efficiency are worsened
Solution Approach 1:
The patent implements automated event correlation and analysis capabilities that enable the system to self-monitor and self-analyze events without manual intervention. The event correlation engine automatically applies correlation rules, identifies patterns, detects anomalies, and generates insights from event data. This automation maintains detailed analysis capability while eliminating time consumption associated with manual monitoring, allowing the system to continuously analyze events in real-time without human resource expenditure.
3Loss of information
If all events are processed and analyzed in detail, then comprehensive event coverage is improved, but processing efficiency and resource utilization are worsened
Solution Approach 1:
The patent extracts and focuses processing resources on critical events by applying correlation rules and filtering mechanisms. The event correlation engine processes all incoming events but extracts only those that meet specific correlation criteria or indicate critical conditions. This selective extraction approach maintains comprehensive event coverage by monitoring all events while improving processing efficiency by concentrating detailed analysis only on relevant critical events, thereby resolving the contradiction between complete coverage and processing efficiency.
Data Source
AI summary
A system includes processing circuitry; and a memory connected to the processing circuitry, wherein the memory is configured to store executable instructions that, when executed by the processing circuitry, facilitate performance of operations, including receive a virtualized radio access network (vRAN) policy that includes an event to filter for and response to the event; obtain event messages from a data source, wherein each event message is generated by one or more state changes within a vRAN; filter each event message based on the vRAN policy; enrich filtered event message with information from an inventory; and trigger an action corresponding to the response in the vRAN policy.