ML Network Management Platform for Latency Reduction
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Solution Overview
Problem
As demand for mobile devices and IoT devices increases, network service providers face challenges in maintaining network performance due to increased device access and data usage, leading to potential decreases in quality of service, network latency, and customer churn without infrastructure improvements.
Innovation Solution
A network management platform uses machine learning to analyze historical and real-time network data, training data models to generate recommendations for improving network performance by determining efficient routing, resource allocation, and infrastructure adjustments such as commissioning or decommissioning data centers and rerouting traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network infrastructure is improved to handle increased device access and data usage, then network performance and quality of service are maintained, but infrastructure costs and resource consumption increase
Solution Approach 1:
The patent changes the operational parameters of existing data centers dynamically based on real-time demand. Instead of adding infrastructure, the system adjusts power states, resource allocation, and traffic routing parameters to optimize performance. This allows the network to handle increased demand by efficiently utilizing existing resources in different operational states rather than deploying new infrastructure.
Solution Approach 2:
The patent implements dynamic infrastructure management where data centers transition between active, standby, and power-off states based on real-time network demand. This dynamic approach allows the system to scale capacity up or down as needed, maintaining high reliability during peak demand while conserving resources during low-demand periods, thus resolving the contradiction between performance and resource consumption.
2Productivity
If more data centers are commissioned to handle increased traffic, then network capacity increases, but operational complexity and management difficulty increase
Solution Approach 1:
The patent implements self-service through automated machine learning models that autonomously manage data center operations. The system automatically monitors network demand, predicts future requirements, and executes decisions about powering data centers on or off without human intervention. This automation eliminates the operational complexity that would otherwise arise from manually managing multiple data centers, allowing high network capacity to be achieved without proportional increases in management complexity.
Solution Approach 2:
The patent employs continuous feedback loops where the machine learning system monitors network performance metrics, user demand patterns, and data center operational status in real-time. This feedback enables the system to automatically adjust data center power states and resource allocation to maintain optimal capacity while simplifying management through closed-loop control rather than complex manual coordination.
3Measurement precision
If real-time network monitoring and analysis is implemented, then network performance optimization is improved, but computational resource consumption and processing time increase
Solution Approach 1:
The patent applies preliminary action by using machine learning models to predict future network demand and performance requirements based on historical patterns and current trends. Instead of reacting to every real-time fluctuation with intensive computation, the system pre-calculates optimal data center power states and resource allocations based on predicted future conditions. This reduces real-time computational burden while maintaining high measurement precision for performance optimization.
Solution Approach 2:
The patent implements partial monitoring by focusing computational resources on the most critical performance metrics and data centers that have the greatest impact on overall network performance. Rather than continuously analyzing every parameter at every data center with equal intensity, the system applies intensive monitoring selectively to high-priority areas, reducing overall computational resource consumption while maintaining effective performance optimization.
Data Source
AI summary
A device may receive one or more data models that have been trained on a set of historical network performance indicators. The set of historical network performance indicators may include metrics associated with measuring network performance for one or more data centers. The device may receive network data for a group of user devices that are actively using the one or more data centers for network services. The device may determine a set of network performance indicators for the one or more data centers. The device may generate, by providing the set of network performance indicators as input to the one or more data models, one or more recommendations associated with improving network performance. The device may perform, after generating the one or more recommendations, one or more actions associated with improving network performance.