Tiered Network Access Management Using Telemetry

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Solution Overview

Problem

Current information handling systems face challenges in optimizing data traffic across networks due to variations in user tiers and metadata characteristics, leading to inefficient resource allocation and potential network congestion.

Innovation Solution

An information handling system that employs a processor to execute a tiered communication network access policy, utilizing a machine learning algorithm to predict network resource use based on telemetry data, and reallocates endpoint devices across available communication channels and access points to optimize data traffic.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional network access management is used without tiered policies and machine learning, then system complexity is low, but network resource utilization efficiency deteriorates due to inability to optimize allocations based on user tiers and metadata characteristics

Engineering Contradiction:

Improvenetwork resource utilization efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent segments network access management into tiered levels (premium, standard, basic) based on user metadata characteristics. Each tier receives differentiated resource allocations and prioritization, enabling efficient network resource utilization by matching service levels to user needs without requiring complex individualized management for each endpoint device.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary classification of endpoint devices into tiers based on metadata characteristics before network access occurs. This pre-segmentation allows the network to proactively optimize resource allocations and apply appropriate policies, improving efficiency without adding complexity during real-time network operations.

Inventive Principle:

Principle #10Preliminary action

2Reliability

If dynamic reallocation of endpoint devices across communication channels is implemented, then network congestion is reduced and quality of service improves, but system complexity increases due to machine learning algorithms and real-time monitoring requirements

Engineering Contradiction:

Improvequality of serviceVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent implements a feedback mechanism where the machine learning model continuously monitors network conditions, endpoint device performance, and tier violations. Based on this feedback, the system dynamically reallocates endpoint devices across communication channels to maintain quality of service while preventing network congestion. The feedback loop enables adaptive optimization without requiring overly complex manual intervention.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system enables self-service through automated machine learning-driven reallocation of endpoint devices. The ML model independently analyzes network conditions and performs reallocations without human intervention, reducing the operational complexity while maintaining high reliability and quality of service through intelligent, data-driven decisions.

Inventive Principle:

Principle #25Self-service

3Measurement precision

If machine learning algorithms are used to predict network resource use, then resource allocation accuracy improves, but computational requirements and system complexity increase

Engineering Contradiction:

Improveresource use prediction accuracyVSAvoidcomputational complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent applies machine learning algorithms selectively to specific network segments and tier categories rather than uniformly across the entire network. The ML model focuses on predicting resource usage patterns for each tier (premium, standard, basic) based on relevant metadata characteristics, achieving high prediction accuracy while minimizing unnecessary computational overhead by processing only the most relevant data locally.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS11470014B2System and method of managing data connections to a communication network using tiered devices and telemetry data

Publication Date: 2022.10.11 DELL PROD LP

AI summary

An information handling system may include a processor; a memory; the processor to execute computer code of an evolved packet core to initiate a tiered communication network access policy by: detecting the connection of each of a plurality of endpoint devices to a communication network via one of a plurality of access points; and determining if a communication channel among a plurality of communication channels is available on the communication network for each of the endpoint devices based on a tier assigned to each of the endpoint devices; the processor to execute computer code of a telemetry data module to: receive telemetry data descriptive of the use characteristics of the endpoint devices; and execute a communication network machine learning algorithm using the telemetry data to generate a network prediction model; the processor to execute computer code of a reallocation module to: predict network resource use across the communication channels of the communication network based on the network prediction model and, with the reallocation module, reallocate endpoint devices based on the predicted network resource use and tier assigned to the endpoint devices.