Node Pool Optimization for Pod Eviction and Instance Cost Control

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Solution Overview

Problem

Existing container orchestration platforms face inefficiencies in resource optimization, cost-effective scaling, and dynamic adaptation, leading to issues such as pod eviction and suboptimal resource utilization due to poorly selected instance types and max pod counts.

Innovation Solution

A node pool optimizer device that determines optimal instance types and max pod counts based on resource requirements and operational costs, using iterative calculations to prevent pod evictions and minimize costs.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If static instance types and max pod counts are used for node pools, then device complexity is reduced, but resource utilization efficiency deteriorates due to inability to adapt to changing workload demands

Engineering Contradiction:

Improvedynamic adaptation to changing resource demandsVSAvoidcomplexity of node pool optimization system

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements dynamic node pool optimization by continuously calculating optimal instance types and max pod counts based on real-time resource utilization data. The system transitions from static configurations to dynamic adjustments, allowing node pools to automatically adapt to changing workload demands through iterative optimization calculations that update node pool parameters without manual intervention.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system incorporates feedback mechanisms by monitoring actual resource utilization metrics and using this information to refine node pool configurations. The optimization process continuously loops through resource consumption data, compares it against target utilization thresholds, and adjusts node pool parameters accordingly, creating a closed-loop feedback system that improves adaptability over time.

Inventive Principle:

Principle #23Feedback

2Reliability

If node pools are oversized to prevent pod evictions, then reliability improves, but resource utilization efficiency deteriorates due to wasted capacity

Engineering Contradiction:

Improveprevention of pod evictionsVSAvoidwasted computational resources

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The patent dynamically adjusts critical parameters including max pod count and instance type selections based on real-time resource utilization patterns. By changing these parameters rather than maintaining fixed values, the system can precisely match node pool capacity to actual demand, preventing both pod evictions and wasted resources through data-driven parameter optimization.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system performs preliminary calculations to determine optimal node pool configurations before pod evictions can occur. By proactively adjusting node pool parameters based on predicted resource consumption patterns, the system prevents pod evictions in advance while avoiding the creation of oversized node pools that would waste resources.

Inventive Principle:

Principle #10Preliminary action

3Productivity

If manual intervention is used to select instance types, then ease of operation improves, but productivity deteriorates due to time-consuming optimization processes

Engineering Contradiction:

Improvespeed of node pool optimizationVSAvoiduser involvement in optimization

Core Design Contradiction:

ProductivityVSEase of operation

Solution Approach 1:

The system implements self-service optimization by automatically performing node pool configuration calculations without requiring user intervention. The optimization process autonomously analyzes resource utilization data, selects optimal instance types, and adjusts node pool parameters, freeing users from manual configuration tasks while maintaining high optimization speed and accuracy.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical processes with automated computational systems. Instead of users manually selecting and configuring node pool parameters, the system uses algorithmic optimization processes that automatically calculate and adjust configurations based on resource data, significantly improving productivity while reducing operational complexity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

4Productivity

If multiple instance types are evaluated for optimal node pool configuration, then resource utilization efficiency improves, but device complexity increases due to iterative calculations

Engineering Contradiction:

Improveresource allocation efficiencyVSAvoidcomplexity of optimization calculations

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system applies partial action by evaluating a manageable subset of instance types rather than exhaustively analyzing all possible configurations. The optimization process focuses on the most relevant instance types based on current workload characteristics, achieving sufficient optimization efficiency without the computational complexity of complete exhaustive search.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20260017117A1Dynamically determining node pool data

Publication Date: 2026.01.15 DELL PROD LP

AI summary

Architectures and techniques are described that can dynamically determine certain optimization data with respect to node or node pool configurations. For example, a maximum pod per node (MPPN) value, a recommended instance type (RIT), and a cost per pod (CPP) value can be dynamically determined. The MPPN value can be determined to prevent pod eviction during autoscaling functions associated with a node pool. The RIT can be determined to reduce operational costs that may be higher if a different instance type is used instead.