ML-Based Telecom Cluster Management for Predictive Performance
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Solution Overview
Problem
Current approaches are unable to effectively identify circumstances under which clusters of servers can be formed in telecommunications networks, leading to inefficiencies and stress on network components.
Innovation Solution
A method using machine learning algorithms to determine the number of clusters based on telecommunications infrastructure data, predict their performance, and generate reports for user devices, employing a data collection and forecasting engine, a data analytics engine, and a knowledge lake to analyze and visualize cluster performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If servers are connected to form a pool to alleviate stress on components, then the stress on individual components is reduced, but the ability to identify optimal cluster configurations is lost
Solution Approach 1:
The system performs self-service by automatically determining optimal cluster configurations and device assignments without requiring manual intervention. The machine learning model autonomously analyzes infrastructure data, identifies clustering opportunities, and generates configuration recommendations, allowing the system to manage its own optimization needs.
Solution Approach 2:
The patent replaces manual mechanical configuration processes with an automated machine learning-based system. Instead of manually analyzing infrastructure data and determining cluster configurations, the system uses ML algorithms to automatically process data, identify patterns, and generate optimization recommendations.
2Productivity
If manual approaches are used to manage device clusters, then implementation is simple, but optimal configurations cannot be identified leading to inefficiencies
Solution Approach 1:
The patent replaces manual mechanical configuration processes with an automated machine learning-based system. Instead of manually analyzing infrastructure data and determining cluster configurations, the system uses ML algorithms to automatically process data, identify patterns, and generate optimization recommendations.
Solution Approach 2:
The system implements feedback by continuously monitoring telecommunications infrastructure data and using machine learning models to evaluate cluster performance. The model learns from historical data and performance metrics, refining its clustering recommendations over time to improve network efficiency and resource utilization.
3Productivity
If machine learning algorithms are used to determine cluster configurations, then optimal configurations are identified improving efficiency, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary layer consisting of the machine learning model that sits between the raw infrastructure data and the cluster configuration decisions. This intermediary processes and interprets complex data, transforming it into actionable clustering recommendations, thereby managing system complexity while maintaining high productivity.
4Reliability
If clusters are formed without automated identification, then implementation is straightforward, but performance optimization is lost
Solution Approach 1:
The system performs self-service by automatically determining optimal cluster configurations and device assignments without requiring manual intervention. The machine learning model autonomously analyzes infrastructure data, identifies clustering opportunities, and generates configuration recommendations, allowing the system to manage its own optimization needs.
Solution Approach 2:
The system implements feedback by continuously monitoring telecommunications infrastructure data and using machine learning models to evaluate cluster performance. The model learns from historical data and performance metrics, refining its clustering recommendations over time to improve network efficiency and resource utilization.
Data Source
AI summary
A method comprises receiving telecommunications infrastructure data corresponding to a plurality of devices, and determining a number of a plurality of clusters comprising respective subsets of the plurality of devices. The determination is based on at least a portion of the telecommunications infrastructure data and is performed using at least one machine learning algorithm. The plurality of clusters are identified and performance of respective ones of the plurality of clusters is predicted using the at least one machine learning algorithm. The method further comprises generating a report including the predicted performance of the respective ones of the plurality of clusters and causing transmission of the report to one or more user devices.