Automated POD Topology Mapping for Network Cloud Maintenance
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Solution Overview
Problem
The current Airship platform lacks an end-to-end diagnostic tool for effectively managing network maintenance in wireless network environments, making network service assurance and maintenance difficult and time-consuming due to the complexity of containerized environments and software protocols.
Innovation Solution
A system and method are introduced that include a network cloud configured for a point of deployment (POD) environment with a processor and memory to establish a POD, map its topology, and troubleshoot issues based on predefined rules, including confirming connections, testing configurations, and correcting errors, using components like Shipwright Orchestrator, Ship Monitor, and Ship Tester to automate the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual troubleshooting methods are used for network cloud issues, then human expertise can be applied to complex problems, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system enables self-service troubleshooting by automatically executing diagnostic routines, collecting performance metrics, and attempting repairs without requiring continuous human intervention. The automated diagnostic system performs maintenance tasks independently, reducing the time operators need to spend on routine troubleshooting while preserving expertise for complex analysis.
Solution Approach 2:
The patent replaces manual mechanical troubleshooting processes with an automated electronic system that collects data from network components, analyzes performance metrics, executes diagnostic routines, and performs repairs automatically. This substitution eliminates time-consuming manual operations while maintaining comprehensive diagnostic capability through electronic monitoring and analysis systems.
2Difficulty of detecting and measuring
If comprehensive monitoring of all network components is implemented, then complete diagnostic capability is achieved, but system complexity increases
Solution Approach 1:
The monitoring system is segmented into modular components including performance metric collection modules, diagnostic routine modules, and repair execution modules. Each module handles specific aspects of monitoring and diagnosis independently, making the overall complex system manageable through functional decomposition while maintaining comprehensive diagnostic capability across all network components.
Solution Approach 2:
The system employs universal monitoring components that can collect performance metrics from multiple different network components through standardized interfaces. This multi-functionality allows a single monitoring framework to handle diverse network elements without requiring separate specialized monitoring systems for each component type, thereby reducing overall system complexity.
3Speed
If automated troubleshooting routines are implemented, then maintenance speed increases, but adaptability to unique problems decreases
Solution Approach 1:
The system performs preliminary actions by pre-defining diagnostic routines and repair procedures for common network issues. When problems occur, the automated system executes these pre-prepared routines immediately, achieving fast maintenance speeds for typical issues. The preliminary preparation of diagnostic and repair protocols enables rapid automated response without sacrificing adaptability, as the system can select appropriate pre-defined routines based on detected problem patterns.
Solution Approach 2:
The automated troubleshooting system incorporates feedback mechanisms that continuously monitor network performance metrics and adjust diagnostic routines accordingly. When automated routines detect issues not covered by standard procedures, the feedback loop enables the system to adapt by collecting additional data, analyzing patterns, and modifying subsequent diagnostic and repair actions, thereby maintaining versatility while operating at high speed.
Data Source
AI summary
A system comprising a network cloud configured for a point of deployment containerized environment, a plurality of servers in communication with the network cloud, configured to establishing a point of deployment (POD) in one of the plurality of servers, receiving a determination that the POD is not operational, mapping the topology of the POD, and based on the mapping step, troubleshooting the POD in accordance with a set of rules.