Predictive Network Resource Scaling for Reliability and Energy Use

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Solution Overview

Problem

Existing network computing systems face inefficiencies due to varying resource demands, leading to system failures, energy wastage, and increased latency, as they either maintain excessive resources or fail to adapt promptly to demand fluctuations.

Innovation Solution

A self-optimizing network system using machine learning to predict resource needs based on historical data and real-time metrics, dynamically scaling computing resources to match demand, thereby preventing failures and optimizing resource usage.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If computing resources are maintained at high levels to prevent system failures, then system reliability is improved, but energy consumption increases

Engineering Contradiction:

Improvesystem reliabilityVSAvoidenergy consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The system dynamically adjusts computing resources based on real-time network conditions and traffic patterns. Virtual machines are scaled up or down automatically in response to changing demands, allowing the system to maintain reliability when needed while reducing energy consumption during low-traffic periods.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system implements feedback mechanisms that monitor network performance, traffic volume, and resource utilization continuously. This feedback drives automated decisions about resource allocation, ensuring system reliability is maintained through proactive resource adjustment rather than static over-provisioning.

Inventive Principle:

Principle #23Feedback

2Loss of energy

If computing resources are scaled down to reduce energy wastage, then energy efficiency is improved, but system reliability deteriorates

Engineering Contradiction:

Improveenergy wastageVSAvoidsystem reliability

Core Design Contradiction:

Loss of energyVSReliability

Solution Approach 1:

The system performs preliminary actions by proactively provisioning computing resources before traffic spikes occur. Machine learning models predict future network conditions and pre-allocate virtual machines in advance, preventing system failures while avoiding the need to maintain permanently excess resources.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system employs self-service automation through orchestration platforms that automatically provision, scale, and manage computing resources based on monitored conditions. This eliminates manual intervention while ensuring reliability through automated resource management that responds dynamically to network demands.

Inventive Principle:

Principle #25Self-service

3Productivity

If computing resources are increased to handle peak traffic, then network performance is improved, but resource utilization efficiency decreases

Engineering Contradiction:

Improvenetwork performanceVSAvoidresource utilization efficiency

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

The system dynamically scales computing resources to match actual network traffic patterns. Virtual machines are activated or deactivated based on real-time conditions, ensuring high network performance during peak traffic while maintaining low resource utilization during off-peak periods, thus improving overall efficiency.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes operational parameters such as the number of active virtual machines, CPU allocation, and memory resources based on network conditions. This allows the system to optimize network performance when traffic is high while reducing resource consumption when traffic is low, improving overall utilization efficiency.

Inventive Principle:

Principle #35Parameter changes

4Reliability

If redundant computing resources are maintained to prevent failures, then system availability is improved, but cost increases

Engineering Contradiction:

Improvesystem availabilityVSAvoidcomputing resources

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The system uses machine learning models to predict future network conditions and proactively provisions computing resources before failures occur. This eliminates the need for permanently redundant resources while maintaining high availability through intelligent, predictive resource management.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements self-service automation that automatically manages computing resources based on monitored conditions and predictive analytics. This reduces the need for manual over-provisioning of redundant resources while maintaining system availability through automated responses to changing network conditions.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20260095374A1Self-optimizing networks

Publication Date: 2026.04.02 BOOST SUBSCRIBERCO LLC

AI summary

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for self-optimizing networks. In some implementations, a method for self-optimizing networks includes obtaining information indicating performance metrics of a first set of computing resources of a distributed system; generating information representing usage of a wireless network at a first point in time; providing the information to a machine learning model trained to predict network events at a time subsequent to the first point in time; determining that at least one particular network event predicted in the output is addressable by using a second set of computing resources of the distributed system; transmitting a signal configured to adjust the distributed computing system to deploy the second set of computing resources.