Correlation Engine Root Cause Service Impact Analysis
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Solution Overview
Problem
In telecommunication networks with numerous heterogeneous elements, identifying the root cause of failures and their service impact is challenging due to the flood of self-monitoring information, requiring efficient root cause analysis and service impact analysis to quickly issue accurate Trouble Tickets and manage service contracts.
Innovation Solution
A correlation engine is employed, comprising Root Cause Analysis (RCA) and Service Impact Analysis (SIA) modules, which are coupled to network sections to analyze alarms, providing root cause and service impact results that can be flexibly routed through a stack of blocks, allowing for independent implementation and adaptation to network changes, enabling efficient alarm grouping and Trouble Ticket management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network elements are self-monitored to detect failures, then failure detection capability is improved, but information flood occurs making root cause identification difficult
Solution Approach 1:
The patent segments the network monitoring system into multiple correlation engines, each responsible for a specific network section. Each correlation engine processes alarms from its designated section independently, dividing the overwhelming global alarm flood into manageable local segments. This segmentation allows root cause analysis to be performed on smaller subsets of alarms rather than the entire network's alarm flood simultaneously.
Solution Approach 2:
The correlation engine acts as an intermediary between self-monitoring network elements and operators. It receives raw alarm information from multiple network elements, processes this information through root cause analysis algorithms, and presents filtered, analyzed results to operators. This intermediary layer transforms the information flood into structured, actionable intelligence.
2Area of stationary object
If numerous network elements are monitored, then monitoring coverage is improved, but complexity of identifying root cause and service impact increases
Solution Approach 1:
The monitoring system is divided into multiple correlation engines, each handling a specific network section. This segmentation maintains comprehensive monitoring coverage across the entire network while limiting the analysis complexity at each individual engine to only its local section's alarms and dependencies.
Solution Approach 2:
Each correlation engine performs partial analysis on its local section's alarms rather than attempting complete global analysis. The root cause analysis and service impact analysis are performed partially on each section independently, with results aggregated to provide comprehensive network-wide insights without requiring each engine to handle the full complexity of the entire network.
3Loss of time
If rapid root cause identification is implemented, then service repair time is reduced, but resource requirements for analysis increase
Solution Approach 1:
The system segments the computationally intensive root cause analysis across multiple correlation engines operating in parallel on different network sections. This distribution of processing workload reduces the resource burden on any single engine while maintaining rapid overall analysis throughput, as multiple engines simultaneously process different portions of the network's alarm data.
Data Source
AI summary
A network correlation engine (CE-1) to be coupled to a telecommunication network (NT) comprising at least one network section (Sm) to supply at least one event notification (Am) upon detection of an event relating to the section. The correlation engine comprises at least one event analysis block (Bn) comprising at least a root cause analysis module (RCn) to receive on input the event notification and to supply on output a root cause analysis result (RRn) and a service analysis module (SAn) to receive on input the root cause analysis result from the root cause analysis module of the block is and to supply on output a service impact analysis result (SRn).