Network Object Grouping via KPI Vector Analysis

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Solution Overview

Problem

Existing methods for identifying network object groups in dynamic and heterogeneous communication networks are inefficient, relying on manual topology tracking and specialized protocols that do not support multi-vendor networks or networks with inexplicit identifiers.

Innovation Solution

A method and apparatus that group network objects based on common key performance indicators (KPIs), representing them as data vectors and comparing these vectors to determine relation values, thereby identifying network object groups at hierarchical levels without requiring special protocols or probes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual topology tracking is used to identify network object groups, then the identification process can be performed with existing tools, but the device complexity and time consumption increase significantly in dynamic virtualized networks

Engineering Contradiction:

Improvenetwork object group identification efficiencyVSAvoidtopology tracking complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent replaces manual topology tracking mechanisms with an automated statistical analysis system that uses traffic pattern data and machine learning algorithms to identify network object groups, eliminating the need for manual intervention in dynamic virtualized environments

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables network objects to self-organize into groups based on their traffic patterns and relationships, with the automated analysis system continuously discovering and updating group structures without external management input

Inventive Principle:

Principle #25Self-service

2Adaptability or versatility

If specialized topology discovery protocols are used to identify network connections, then connection information can be obtained, but compatibility with multi-vendor networks and networks with inexplicit identifiers is lost

Engineering Contradiction:

Improvemulti-vendor network compatibilityVSAvoidtopology information availability

Core Design Contradiction:

Adaptability or versatilityVSLoss of information

Solution Approach 1:

The patent creates a universal analysis framework that processes traffic pattern data from heterogeneous network sources using standardized statistical methods, enabling compatibility across multi-vendor environments while maintaining the ability to discover topology relationships

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system introduces traffic pattern statistics as an intermediary that indirectly reveals network relationships without requiring direct protocol exchanges between network objects, enabling topology discovery in networks with inexplicit identifiers

Inventive Principle:

Principle #24Intermediary (Mediator)

3Reliability

If network objects are grouped based on physical topology information, then the grouping reflects actual network connections, but the approach fails in virtualized networks where physical topology is not readily available

Engineering Contradiction:

Improvegrouping accuracyVSAvoidtopology information accessibility

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The patent transitions from using physical topology parameters to using traffic pattern statistical parameters for grouping network objects, maintaining grouping accuracy in virtualized environments where physical topology information is inaccessible

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentEP3560145B1Apparatus and method for identifying network object groups

Publication Date: 2020.11.25 TELEFONAKTIEBOLAGET LM ERICSSON (PUBL)

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AI summary

A method of identifying a network object group from a plurality of network objects (x1 to xM) of a communications network comprises grouping network objects based on a function of a plurality of key performance indicators, KPIs (k1 to kN), which are common to the plurality of network objects (x1 to xM).