Auto-Scaling Data Layers for SLA-Stable Load Spikes

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Solution Overview

Problem

Widely distributed data ingestion systems experience data load spikes due to debugging or additional devices, impacting Service Level Agreements (SLAs) in data transfer rates.

Innovation Solution

Implement a universal auto-scaling method using an auto-scale computer program to monitor utilization levels across data layers, deploy additional nodes when thresholds are met, and remove nodes when utilization is low, utilizing machine learning for proactive threshold adjustments.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual scaling is used to handle data load spikes, then resource utilization can be optimized, but the system cannot respond automatically to sudden changes impacting SLA

Engineering Contradiction:

Improvedata transfer rateVSAvoidService Level Agreement compliance

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system implements self-service through an auto-scaling mechanism that automatically monitors utilization levels across data layers and deploys or removes nodes based on predefined thresholds, eliminating the need for manual intervention while maintaining SLA compliance during load spikes

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system uses feedback by continuously monitoring utilization levels at each data layer and comparing them against thresholds to trigger automatic scaling actions, creating a closed-loop control system that responds to actual system state changes

Inventive Principle:

Principle #23Feedback

2Productivity

If additional nodes are deployed to handle data load spikes, then data transfer rate is maintained, but resource waste occurs when loads are low

Engineering Contradiction:

Improvedata transfer rateVSAvoidresource waste

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

The system applies dynamics by making the node configuration flexible and adjustable based on actual load conditions, allowing the system to transition between different scaling states (add nodes when utilization exceeds threshold, remove nodes when below threshold) to optimize resource usage

Inventive Principle:

Principle #15Dynamics

3Ease of operation

If manual monitoring and scaling is performed, then control over resource allocation is maintained, but response time to load changes increases

Engineering Contradiction:

Improvecontrol over resourcesVSAvoidresponse time to load changes

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The system replaces manual mechanical operations with automated computer-programmed processes that monitor utilization levels and execute scaling actions automatically, significantly reducing response time from manual intervention to automated real-time response

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12579006B2Systems and methods for universal auto-scaling

Publication Date: 2026.03.17 JPMORGAN CHASE BANK NA

AI summary

Systems and methods for universal auto-scaling are disclosed. In one embodiment, a method may include: (1) monitoring, by an auto-scale computer program executed by a computer processor, a utilization level at each of a plurality of data layers in a data pod, wherein each data layer comprises at least one node; (2) comparing, by the auto-scale computer program, each of the utilization levels to a threshold; (3) identifying, by the auto-scale computer program, that one of the thresholds is met or exceeded; and (4) deploying, by the auto-scale computer program, an additional node to the data layer with the met or exceeded utilization level.