Container Cluster Recommendation Prioritization With Readiness Scoring
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Solution Overview
Problem
Current container orchestration systems lack automated, efficient methods for prioritizing recommendations and quantifying the impact of resource changes, leading to decreased efficiency and stability in cluster management.
Innovation Solution
A system using scored knowledge transform graphs and a readiness assessment model to prioritize recommendations, identify errors, and quantify production readiness, incorporating generative AI for error analysis and resource optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated recommendation prioritization is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent introduces a scored knowledge transform graph as an intermediary computational structure that mediates between raw recommendation data and prioritization outcomes. This graph transforms unstructured recommendation information into structured, score-based priorities through intermediate processing steps, resolving the contradiction by adding computational mediation rather than direct complex decision logic
Solution Approach 2:
The system changes parameters by introducing multiple scoring dimensions (discrepancy score, confidence score, readiness scores) to transform qualitative recommendation data into quantitative prioritization metrics. This parameter transformation approach enables automated prioritization through mathematical scoring rather than complex rule-based decision-making
2Measurement precision
If discrepancy scores are generated for each recommendation, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent applies partial action by calculating discrepancy scores selectively for recommendations that meet certain criteria rather than computing all possible metrics for every recommendation. This selective scoring approach provides sufficient measurement precision for prioritization while avoiding the time cost of exhaustive analysis of all recommendations
3Reliability
If confidence scores and readiness assessment models are used, then reliability is improved, but device complexity increases
Solution Approach 1:
The readiness assessment model is segmented into multiple independent scoring components (discrepancy score, confidence score, readiness scores) that evaluate different aspects of cluster health separately. This segmentation allows each component to be simple and focused while collectively providing comprehensive reliability assessment, avoiding the need for a single complex monolithic model
Data Source
AI summary
Computer-implemented methods for recommendation prioritization for a container orchestration system. Aspects include receiving a set of recommendations for a cluster of a container orchestration system. Aspects also include selecting an optimal recommendation from the set of recommendations using a scored knowledge transform graph. Aspects further include generating a confidence score for the cluster based on the optimal recommendation. Aspects also include determining a category of a readiness assessment model for the cluster using the confidence score. Aspects further include modifying a computer resource of the cluster based on the category of the readiness assessment model.