Network Impact Assessment System for Configuration Change Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In large networks, configuration changes, such as deploying new filters or modifying routes, often have unforeseen effects that can lead to network disruptions, causing inconvenience to users due to the difficulty in predicting the extent of their impact before implementation.
Innovation Solution
A system and method for predicting the outcomes of network configuration changes by assessing the impact on affected devices, which involves a request for a network impact assessment, recursive distribution of the assessment request, aggregation of responses, and calculation of an impact factor to determine whether to automatically approve or implement the change.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If configuration changes are implemented without impact assessment, then network deployment speed is improved, but network reliability deteriorates due to unforeseen disruptions
Solution Approach 1:
The system performs impact assessment before implementing configuration changes by recursively querying network devices to determine affected devices and potential disruptions. This preliminary evaluation allows administrators to anticipate consequences and make informed decisions about whether to proceed with changes, thereby preventing unforeseen network disruptions while maintaining efficient deployment practices
2Reliability
If comprehensive impact assessment is performed for every configuration change, then network reliability is improved, but the time required before implementation increases
Solution Approach 1:
The impact assessment process is segmented into recursive queries distributed across multiple network devices. Each device independently evaluates its own impact and forwards the assessment request to neighboring devices. This segmentation allows parallel processing of impact assessments across the network, reducing total assessment time while maintaining comprehensive coverage of all potentially affected devices
Solution Approach 2:
The system implements a feedback mechanism where network devices respond to assessment requests with information about their own impact status. These responses propagate back through the network, allowing the originating device to aggregate impact information and make informed decisions. The feedback loop enables efficient information gathering without requiring centralized analysis of the entire network state
3Measurement precision
If manual approval process is used for configuration changes, then decision quality is improved, but productivity decreases due to approval delays
Solution Approach 1:
The system enables network devices to autonomously participate in impact assessment by automatically responding to queries and maintaining their own configuration state information. This self-service capability allows devices to contribute to decision-making without requiring continuous human intervention, improving both decision accuracy through comprehensive data and implementation speed by reducing manual processing steps
Data Source
AI summary
In large networks, configuration changes made to the network may have effects the extent of which is not easily foreseen before the configuration change is implemented. For example, the deployment of a new filter to the routers of a network may have the effect of disconnecting a larger portion of the network than was expected, and the recognition and correcting of such unintended effects may occur after a sufficient delay that users of the network may be inconvenienced. As such, a system and method for predicting outcomes of network configuration changes are provided.