Container Rightsizing With Integrated Vertical and Horizontal Scaling
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Solution Overview
Problem
Existing container orchestration systems face inefficiencies due to the inability to dynamically combine vertical and horizontal scaling methods, leading to resource wastage and excessive costs, as traditional scaling techniques often interfere with each other, failing to adapt to dynamic resource demands.
Innovation Solution
Implement a system that uses machine learning to analyze metrics from containers, dynamically adjusting both resource allocation and target resource utilization, integrating vertical and horizontal scaling to optimize resource usage and minimize waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual resource allocation based on best practices is used, then application performance can be maintained, but resource wastage and excessive costs occur due to inability to adapt to dynamic changes
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring container metrics and automatically adjusting resource allocation and target utilization settings. The system transitions from static manual configuration to dynamic automated adjustment, allowing resources to adapt in real-time to changing application demands, thereby eliminating resource wastage while maintaining performance
Solution Approach 2:
The system establishes a feedback loop by continuously collecting container metrics, analyzing current resource utilization, and using this information to automatically adjust resource allocation and target utilization settings. This closed-loop control enables the system to respond to actual performance data, optimizing resource distribution dynamically without manual intervention
2Productivity
If vertical scaling is implemented to handle increased workload, then application capacity improves, but resource allocation becomes inefficient and costs increase due to interference with horizontal scaling
Solution Approach 1:
The patent merges vertical and horizontal scaling methods into a unified resource management system. Instead of treating them as separate, potentially conflicting operations, the system integrates both scaling dimensions and automatically determines the optimal combination of vertical resource allocation adjustments and horizontal pod replication based on current workload demands and cost considerations
Solution Approach 2:
The system dynamically changes key parameters including resource allocation limits, target resource utilization percentages, and pod replication counts based on real-time metrics analysis. By adjusting these parameters automatically, the system optimizes the mix of vertical and horizontal scaling operations to match actual workload requirements while minimizing resource wastage and costs
3Adaptability or versatility
If horizontal scaling is used to accommodate unexpected activity changes, then application responsiveness improves, but resource allocation becomes excessive leading to increased costs
Solution Approach 1:
The patent applies partial action by allocating resources and replicating pods based on actual measured needs rather than over-provisioning for worst-case scenarios. The system analyzes container metrics to determine the precise amount of horizontal scaling required, avoiding excessive resource allocation while maintaining sufficient responsiveness to handle unexpected activity changes
Solution Approach 2:
The system implements dynamic horizontal scaling by continuously monitoring container metrics and automatically adjusting pod replication counts in response to changing workload demands. This dynamic approach allows the application to remain responsive to unexpected activity changes while avoiding the costs associated with static over-provisioning
Data Source
AI summary
A method for rightsizing an application, including analyzing, via processing circuitry, metrics from containers; determining, via the processing circuitry, a resource allocation and a target resource utilization for an application workload based on the metrics; and configuring, via the processing circuitry, the application workload based on the resource allocation, the target resource utilization, and the metrics.