Network Fault Originator Identification in Virtual Infrastructure
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Solution Overview
Problem
Conventional systems fail to scale effectively in identifying the root cause of network faults in highly virtualized, real-time, and dynamic environments of SDN, NVP, and UDNC, leading to time-consuming manual data retrieval and delayed analytics due to the lack of mechanisms for measuring alarm analytics performance and providing clear indications of fault origins.
Innovation Solution
A system and method for determining network fault conditions by collecting network event data prior to a polling time, identifying trap and alarm sets associated with fault origins, and generating qualified source tickets using a root cause correlation information model, allowing for real-time identification of root causes and fault origination within virtualized network infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional alarm analytics mechanisms are used in virtualized network infrastructure, then system complexity is reduced and ease of operation is maintained, but fault identification speed deteriorates and productivity decreases due to time-consuming manual data retrieval and delayed analytics
Solution Approach 1:
The system performs preliminary actions by collecting network event data before a polling time and proactively analyzing fault conditions. The fault originator identification system preemptively gathers data, builds correlation information models, and identifies potential faults before they are detected by conventional polling mechanisms, thereby reducing fault identification time without requiring proportional increases in system complexity
Solution Approach 2:
The patent introduces an intermediary fault originator identification system that acts as a mediator between raw network event data and conventional alarm analytics. This intermediary layer collects event data, applies correlation models, and generates enriched fault information that feeds into existing systems, improving productivity while isolating the complexity of the new methodology within a dedicated component
2Measurement precision
If real-time fault analytics are implemented in virtualized networks, then fault identification accuracy is improved, but processor burden increases and energy consumption rises due to continuous data collection and analysis
Solution Approach 1:
The system applies partial action by selectively collecting and analyzing only the network event data necessary for fault identification rather than processing all available data in real-time. The correlation information model focuses computation on relevant event patterns and relationships, achieving high fault identification accuracy while avoiding the processor burden of exhaustive real-time analysis of all network events
Solution Approach 2:
The fault originator identification system employs periodic action by collecting network event data at scheduled intervals rather than continuously processing all data streams. This periodic collection approach, combined with correlation model analysis, maintains high measurement precision for fault identification while significantly reducing processor burden compared to continuous real-time analysis
3Productivity
If manual data retrieval methods are used for fault analysis, then system complexity is minimized, but loss of time increases and productivity decreases due to time-consuming manual processes
Solution Approach 1:
The fault originator identification system implements self-service by automatically collecting network event data, applying correlation models, and generating fault analysis results without requiring manual data retrieval or intervention. The system serves itself by autonomously performing data collection, analysis, and report generation, thereby eliminating time-consuming manual processes and significantly improving fault resolution efficiency
Data Source
AI summary
Concepts and technologies directed to network fault originator identification for virtual network infrastructure are disclosed herein. Embodiments can include a control system that is communicatively coupled with network infrastructure. The control system can include a processor and memory that, upon execution, causes the control system to perform operations. The operations can include determining, based on a source ticket, a network fault condition associated with the network infrastructure. The operations can further include identifying, from the source ticket, a trap set and an alarm set that are associated with origination of the network fault condition. The operations can include the control system collecting network event data from the network infrastructure prior to a polling time of a fault reporting schedule; determining that a qualified source ticket should be created; and generating the qualified source ticket based on the network event data.