Container Backup Load Balancing Using KNN Priority Tagging

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Solution Overview

Problem

Existing container management systems lack intelligent load balancing and prioritized backup solutions for Kubernetes environments, particularly in large-scale data centers, leading to inefficiencies in data protection and recovery.

Innovation Solution

Implementing a KNN-based machine learning model for container classification and priority tagging, which automatically assigns backup priorities based on container attributes and dynamically adjusts backup schedules to balance load across containers.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual backup scheduling is used based on defined backup policies, then backup operations can be controlled and managed, but intelligent load balancing and prioritization of critical containers is missing

Engineering Contradiction:

Improvedata protection reliabilityVSAvoidautomation of backup scheduling

Core Design Contradiction:

ReliabilityVSExtent of automation

Solution Approach 1:

The system automatically classifies containers using KNN-based machine learning and assigns priority tags without manual intervention. The backup scheduler autonomously determines backup sequences and redistributes load based on container priorities, eliminating the need for manual backup policy configuration while maintaining reliable data protection.

Inventive Principle:

Principle #25Self-service

2Productivity

If backup jobs are scheduled without intelligent load balancing, then backup operations can be performed, but load distribution among containers becomes unbalanced leading to inefficiencies

Engineering Contradiction:

Improvebackup operation efficiencyVSAvoidload balancing complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system continuously monitors container attributes and backup progress, using this feedback to dynamically adjust backup scheduling. The KNN classifier re-evaluates container priorities based on current system state, and the scheduler redistributes backup jobs to maintain optimal load balance, improving efficiency through adaptive control.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

Backup schedules are dynamically adjusted based on real-time container priorities and system load conditions. The system transitions from static backup policies to dynamic scheduling that responds to changing container attributes, ensuring optimal resource utilization and balanced load distribution across all containers.

Inventive Principle:

Principle #15Dynamics

3Reliability

If all containers are treated equally in backup operations, then simple backup policies can be applied, but critical containers may not receive prioritized backup attention

Engineering Contradiction:

Improvecritical data protectionVSAvoidoperational simplicity

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system applies different backup priorities and treatment levels to different containers based on their criticality classification. Critical containers receive prioritized backup attention while less critical containers are scheduled accordingly, creating localized quality differentiation that ensures important data is protected without requiring complex manual policies.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS12585551B2Smart load balancing of containers for data protection using supervised learning

Publication Date: 2026.03.24 DELL PROD LP

AI summary

A container load balancer process helps schedule backups of containerized data based on defined attributes and historical data. Containers are classified using a KNN-based classifier based on attributes. A tagger component assigns a priority tag to each container. A monitor monitors an assignment of backup tasks to proxies for the backing up step, and a load balancer determines if the assignment distributes backup loads within a defined performance tolerance, and adjusts the assignment if not. A backup server then backs up the container data in an order determined by the classifying and the assignment or adjusted assignment.