Pod Resource Recommendations From Historical Usage Patterns
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Solution Overview
Problem
Existing systems lack dynamic optimization and actionable recommendations for container resources, leading to overprovisioning, inefficient resource utilization, and lack of visibility into usage and costs, resulting in idle cloud resources and fragmentation.
Innovation Solution
A computer-implemented method and system that calculates request and limit usage for pods using historical data, generates recommendations for optimal resource configurations, and provides real-time validation through machine learning models, enabling efficient resource allocation and reduction of unused resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static resource allocation is used for container pods, then device complexity is reduced, but resource utilization efficiency deteriorates leading to overprovisioning and idle cloud resources
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring pod resource usage metrics and automatically adjusting resource limits based on observed patterns. The system transitions from static pre-defined resource allocation to dynamic adjustment, where resource limits are modified in response to changing workload demands, thereby improving resource utilization efficiency without significantly increasing operational complexity
Solution Approach 2:
The system establishes a feedback loop that monitors pod resource usage, analyzes utilization patterns, and uses this information to generate recommendations for optimizing resource limits. The feedback mechanism compares actual resource consumption against allocated limits and adjusts allocations accordingly, eliminating overprovisioning while maintaining system stability through continuous adaptation
2Extent of automation
If manual resource management is used, then automation level is reduced, but system reliability is maintained through human oversight, yet productivity deteriorates due to increased deployment and management time
Solution Approach 1:
The patent implements self-service automation where the system autonomously monitors pod resource usage, analyzes utilization patterns, and generates optimization recommendations without requiring manual intervention. The automated system performs resource limit optimization tasks independently, significantly reducing deployment and management time while maintaining reliability through algorithmic analysis and validation
Solution Approach 2:
The system performs preliminary analysis of resource usage patterns and generates optimization recommendations in advance, allowing proactive resource allocation adjustments before performance degradation occurs. By anticipating resource needs and pre-configuring optimal limits based on historical data and forecasting, the system reduces reactive management overhead and accelerates deployment processes
3Measurement precision
If detailed resource tracking is implemented, then measurement precision is improved, but device complexity increases due to additional monitoring and analysis systems
Solution Approach 1:
The patent implements a multi-functional monitoring system that simultaneously performs resource usage tracking, pattern analysis, forecasting, and recommendation generation. By consolidating these functions into a unified system rather than separate specialized tools, the patent achieves high measurement precision for resource visibility while minimizing the complexity increase that would result from multiple independent monitoring systems
Solution Approach 2:
The system merges resource monitoring, analytics, and optimization recommendation functions into an integrated platform. By combining data collection, analysis, and action-generation capabilities into a single cohesive system, the patent achieves comprehensive resource visibility and precise measurement without the proportional complexity increase that would result from separate systems for each function
Data Source
AI summary
Embodiments calculate a request usage and a limit usage for a plurality of pods based on historical data, generate a recommendation for pod resources based on the calculated request usage and the calculated limit usage, and output a recommendation output message for approval which corresponds to the recommendation.