Dynamic Node Re-clustering in Computer Networks via Machine Learning
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Solution Overview
Problem
Existing computer network clusters struggle to optimize performance and resource utilization dynamically, especially as conditions in the cluster, network, and availability of nodes change.
Innovation Solution
The use of machine learning models for dynamic re-clustering of nodes, allowing for the identification of optimal clusters and the retrieval of substitute nodes from other domains or networks to enhance performance and resource efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional clusters are used to perform tasks in computer networks, then the system can execute distributed tasks, but the performance and resource utilization cannot be optimized dynamically when conditions change
Solution Approach 1:
The patent implements dynamic re-clustering of nodes based on real-time conditions in the cluster, network, and node availability. The system continuously monitors changes and re-configures clusters dynamically using machine learning models, transforming the static cluster configuration into a dynamic adaptive system that optimizes performance according to current conditions
Solution Approach 2:
The system employs machine learning models that receive feedback from monitoring cluster conditions, network status, and node availability. This feedback loop enables the models to learn from current system state and generate optimized re-clustering configurations, creating a closed-loop control system that continuously improves performance through adaptive decision-making
2Productivity
If machine learning models are used for dynamic re-clustering, then performance and resource efficiency are optimized, but the system complexity increases
Solution Approach 1:
The patent introduces machine learning models as intermediary components between the raw cluster data and the re-clustering decision-making process. These models act as intelligent mediators that process complex inputs (cluster conditions, network status, node availability) and generate optimized configurations, managing system complexity through specialized intermediate processing layers
Solution Approach 2:
The system segments the complex re-clustering problem into distinct processing stages: data collection from multiple sources, machine learning model processing, configuration generation, and implementation. This segmentation divides the overall complex task into manageable modular components, each handling specific aspects of the optimization process
Data Source
AI summary
Methods and systems for the dynamically re-clustering of nodes in clusters to provide optimal performance and/or the most efficient use of resources through the use of machine learning models. Specifically, the methods and systems may determine a cluster that optimally performs and/or has the most efficient use of resources based on a first machine learning model. The methods and system may then retrieve available substitute nodes from other domains and/or networks that may lie outside the cluster, but may nonetheless be available to, or accessed by the cluster. The methods and systems may then generate an additional plurality of clusters using one or more of the original nodes of the originally selected clusters and/or one or more of the available substitute nodes.