Inferential Network Mapping for Non-Addressable Devices
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Solution Overview
Problem
Network mapping systems are ineffective in service provider networks that include non-addressable devices and logical constructs, making it difficult to manage and maintain these complex networks automatically.
Innovation Solution
An inferential network mapping system that uses available topographical information to infer relationships between and among non-addressable network devices and logical constructs by correlating data from various sources, such as subscriber records, CM MAC datastores, STB MAC datastores, and VOD transaction datastores, to generate a map of the network, even in the presence of devices without network presence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If network mapping systems query devices with network addresses to obtain location information, then addressable devices can be mapped accurately, but non-addressable devices and logical constructs cannot be identified or mapped
Solution Approach 1:
The patent introduces intermediate addressable devices (cable modems, set-top boxes) as mediators to indirectly identify non-addressable devices. By querying the location of these addressable devices and determining their physical connections, the system can infer the locations of non-addressable devices without requiring them to have network presence or respond to queries directly.
Solution Approach 2:
Instead of trying to query non-addressable devices directly (which fails), the patent inverts the approach by querying addressable devices and using their known locations to infer the locations of non-addressable devices. This reverse engineering approach allows the system to map devices that cannot be directly identified.
2Adaptability or versatility
If the network includes numerous non-addressable devices in paths between identifiable devices, then network flexibility and device diversity increase, but automated network mapping becomes ineffective
Solution Approach 1:
The system uses self-service by leveraging existing automated query mechanisms for addressable devices and automatically processing the correlation data. The automated system correlates location data from multiple sources, determines physical connections, and generates the network map without human intervention, maintaining automation despite the complexity introduced by non-addressable devices.
Solution Approach 2:
The patent adds a new dimension to network mapping by incorporating logical constructs (VOD service groups, service flow templates) alongside physical devices. This multi-dimensional approach allows the system to map both physical network topology and logical service relationships, providing a comprehensive view that accommodates device diversity while maintaining automation.
3Productivity
If logical constructs like VOD service groups are used to manage network resources, then resource management efficiency improves, but automated identification of these constructs becomes difficult
Solution Approach 1:
The patent applies multi-functionality by using a single correlation analysis mechanism to identify multiple types of entities: physical devices, logical constructs, and their relationships. The system can process different data sources (subscriber records, transaction data, configuration data) through the same correlation engine, making the automated identification process universal and efficient for various network elements.
Solution Approach 2:
The system uses feedback by continuously correlating data from multiple sources and refining the network map based on consistent patterns. When location data from subscriber records, transaction data, and configuration data align, the system confirms the identification of logical constructs and their relationships, providing reliable automated identification that improves resource management efficiency.
Data Source
AI summary
A system and method for determining network relationships with unreliable data. An identifier of an addressable network element may be associated with a non-addressable network element. A first set of data is generated that associates a first identifier of each of a plurality of first addressable network elements in the network to one of a plurality of non-addressable network elements in the network to which the first addressable network elements are connected. A second data is generated that associates the first identifier of each of the plurality of the first addressable network elements to one of a plurality of second identifiers of a second addressable network element. When at least a threshold measure of the first set of first identifiers matches the second set of first identifiers, the second identifier is associated with the non-addressable network element.


