Dynamic Network Map Relevance Filtering for Memory Efficiency
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Solution Overview
Problem
Existing network mapping technologies struggle to efficiently model network usage over time due to rapid changes in network connectivity, leading to large storage requirements and outdated snapshots.
Innovation Solution
A system and method for generating a time-varying network map that uses minimal memory resources by assigning relevance values to network connections based on their persistence and updating the map dynamically, removing connections that are no longer relevant.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a large number of network maps are stored to show network use over time, then the completeness of network use information is improved, but the memory storage requirement increases significantly
Solution Approach 1:
The patent extracts only the essential and relevant connectivity information from complete network maps. Instead of storing entire network maps, it identifies and stores only the connectivity elements that are currently relevant, removing redundant information about connections that no longer exist or are not currently active.
Solution Approach 2:
The patent inverts the traditional approach by not starting with complete network maps and trying to retain useful information, but rather starting with the assumption that most information is redundant and only preserving what is currently relevant. It builds the network model backwards from the current state, keeping only elements that matter.
2Measurement precision
If network maps are updated frequently to reflect rapid connectivity changes, then the accuracy of network representation is improved, but the processing overhead and storage requirements increase
Solution Approach 1:
The patent implements a dynamic network model where the set of tracked connectivity elements changes over time. Instead of maintaining a static comprehensive model, it dynamically adds new connectivity elements when they appear and removes them when they disappear, allowing the model to adapt to changing network conditions without processing unnecessary data.
Solution Approach 2:
The patent applies partial action by not tracking all possible network connections at all times, but only those that are currently relevant. It processes and stores information about connectivity elements on a need-to-know basis, reducing processing overhead while maintaining accuracy for active connections.
3Loss of information
If all network connections are tracked continuously, then the completeness of connectivity information is improved, but the memory efficiency deteriorates
Solution Approach 1:
The patent extracts and retains only the connectivity elements that are currently relevant in the network. It removes redundant information about connections that no longer exist or are not currently active, significantly reducing memory requirements while maintaining completeness of relevant connectivity information.
Solution Approach 2:
The patent implements a discard-and-recover mechanism where connectivity elements are removed from tracking when they become irrelevant and can be recovered or re-added when they become relevant again. This allows the system to manage memory efficiently by discarding unnecessary information and recovering it only when needed.
Data Source
AI summary
Systems, devices, and methods are discussed for memory efficient network use modeling.