Prioritizing Suboptimal Resources in Distributed Computing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data center operations management tools often mistakenly identify suboptimal resources, leading to false positives, which can result in unnecessary corrective actions that waste time and resources, and potentially cause downstream problems.
Innovation Solution
The implementation of automated computer-implemented methods and systems that classify recommended resources based on resource parameters, construct priority models for each class, and compute the likelihood of suboptimality to prioritize remedial measures, such as deleting or migrating resources, thereby reducing human error and improving accuracy in identifying and correcting suboptimal resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If operations management tools identify suboptimal resources using traditional methods, then resource optimization is achieved, but false positive identification increases leading to unnecessary remedial measures
Solution Approach 1:
The patent segments the resource identification process into multiple independent analysis components: performance metric analysis, dependency relationship analysis, and remedial measure impact assessment. Each component evaluates specific aspects separately before integrating results, allowing the system to identify truly suboptimal resources while filtering out false positives through multi-dimensional validation
Solution Approach 2:
The patent introduces an intermediary assessment layer that evaluates the potential impact of remedial measures before execution. This intermediary system analyzes dependency relationships and predicts downstream effects, acting as a mediator between identification and correction to prevent unnecessary remedial measures on resources that appear suboptimal but are actually functioning correctly
2Measurement precision
If systems administrators manually examine each recommended resource to avoid false positives, then accuracy improves, but time consumption and operational complexity increase
Solution Approach 1:
The patent implements self-service through automated priority scoring and confidence level calculation systems. The operations management tool automatically evaluates each recommended resource, assigns priority levels based on multiple factors, and provides confidence scores indicating the likelihood of true suboptimality. This self-assessment capability eliminates the need for manual examination while maintaining high accuracy through algorithmic analysis of performance metrics and dependency relationships
3Productivity
If remedial measures are executed immediately on identified suboptimal resources, then resource optimization speed increases, but system stability decreases due to potential false positives
Solution Approach 1:
The patent applies preliminary action by performing comprehensive impact assessment and dependency analysis before executing remedial measures. The system pre-evaluates potential consequences, identifies critical dependencies that would be affected, and prepares rollback plans in advance. This preliminary verification ensures that only truly suboptimal resources undergo remediation, maintaining system stability while achieving rapid optimization through pre-planned corrective actions
Data Source
AI summary
This disclosure is directed to automated computer-implemented methods and systems for prioritizing recommended suboptimal resources of a data center. Methods and system described herein save time and increase the accuracy of identifying actual suboptimal resources and executing remedial measures to correct the suboptimal resources.