Auto-scaling Data Protection Pods in Clustered Environments

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Solution Overview

Problem

Existing data protection systems in clustered computing environments struggle to proactively scale computing resources to meet fluctuating demands, leading to potential service disruptions and inefficiencies.

Innovation Solution

Implementing a specialized technique that automatically scales resource units (pods) based on a combination of current workload, resource utilization, and historical data, ensuring that data protection services can efficiently handle varying client requests.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If built-in resource management tools are used to manage computing resources, then resource management is simplified, but the system cannot proactively scale resources to meet fluctuating demands

Engineering Contradiction:

Improveresource managementVSAvoidresource scaling capability

Core Design Contradiction:

Ease of operationVSAdaptability or versatility

Solution Approach 1:

The system performs preliminary actions by analyzing historical workload data and predicting future resource requirements before demand actually occurs. This allows proactive scaling of resource units ahead of time, ensuring resources are ready before needed while maintaining ease of operation through automated predictions.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms by continuously monitoring current workload metrics and comparing them against historical data and predicted trends. This feedback loop enables dynamic adjustment of resource allocation, allowing the system to adapt resource scaling decisions based on real-time conditions while maintaining automated management.

Inventive Principle:

Principle #23Feedback

2Reliability

If resources are scaled to meet peak demand, then service reliability is improved, but resource utilization efficiency decreases

Engineering Contradiction:

Improveservice availabilityVSAvoidresource utilization efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system applies dynamics by continuously adjusting resource unit scaling based on predicted workload trends rather than static over-provisioning. Resources are scaled dynamically to match anticipated demand, ensuring service reliability during peak periods while optimizing utilization by reducing resources during lower-demand periods.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes parameters by adjusting the number of resource units based on multiple factors including current workload, historical patterns, and predicted future demand. This parameter adjustment strategy ensures sufficient resources for reliability while optimizing overall utilization efficiency through data-driven decisions.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If more resource units are deployed, then handling capacity increases, but system complexity and resource consumption increase

Engineering Contradiction:

Improveworkload handling capacityVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system implements self-service by automatically managing resource unit deployment and scaling decisions based on workload analysis and predictions. This eliminates the need for complex manual configuration and management, allowing the system to handle increased workload capacity while keeping operational complexity manageable through automation.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system applies universality by using a single automated resource management mechanism that handles multiple functions: monitoring workload, analyzing historical data, predicting future needs, and executing scaling decisions. This multi-functional approach increases handling capacity while avoiding the complexity of multiple separate management systems.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12294531B2Intelligent auto-scaling of components providing data protection services within a clustered environment

Publication Date: 2025.05.06 DELL PROD LP

AI summary

Methods (and systems) described herein provide a specialized technique for automatically scaling resources that provide data protection services to client systems. In some embodiments, the resource units (or pods) may be deployed on a platform (e.g., platform-as-a-service, or PaaS), which provides containerized services (and workloads) as part of a clustered computing environment (e.g., Kubernetes). The technique may scale the number of resource units used to perform data protection operations based on specialized criteria. For example, a particular service (e.g., redirection service) that utilizes resource units (e.g., pods) to perform functions may automatically increase (or decrease) the number resource units available to handle data management operations originating from the client systems.