Storage Hardware Sizing Using Performance Headroom Metrics

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Solution Overview

Problem

Existing data storage systems face challenges in determining the minimum hardware configuration required to handle scaled-up workloads while maintaining the same workload pattern, leading to potential insufficiency or excess in hardware resources.

Innovation Solution

A method is employed to determine a new hardware configuration by analyzing current hardware utilization and workload patterns, using metrics like load reserve and scale factors to calculate the minimum hardware resources needed for a scaled-up workload, considering front-end, back-end, and data path components.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If hardware resources are increased to handle scaled-up workload, then workload capacity is improved, but hardware cost and complexity increase

Engineering Contradiction:

Improveworkload capacityVSAvoidhardware configuration

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system performs preliminary analysis of current hardware utilization and workload patterns before scaling, using load reserve metrics and scale factors to predict future hardware requirements. This allows organizations to plan hardware upgrades in advance rather than reactively adding resources, optimizing the timing and configuration of hardware additions to match actual workload demands.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The invention changes the parameter of hardware configuration by using quantitative metrics (load reserve, scale factors) to determine optimal hardware specifications. Instead of arbitrary or over-provisioned hardware configurations, the system calculates specific hardware parameters needed to handle scaled workloads efficiently, transforming hardware sizing from a qualitative estimate to a quantitative determination.

Inventive Principle:

Principle #35Parameter changes

2Reliability

If hardware resources are increased to ensure sufficient capacity, then reliability is improved, but resource waste increases

Engineering Contradiction:

Improveworkload support capabilityVSAvoidhardware resource waste

Core Design Contradiction:

ReliabilityVSLoss of substance

Solution Approach 1:

The system implements feedback by continuously monitoring current hardware utilization metrics and using them to inform future hardware configuration decisions. Load reserve measurements and scale factor calculations provide feedback loops that adjust hardware provisioning based on actual system performance and workload characteristics, preventing both over-provisioning and under-provisioning.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The invention applies parameter changes by using calculated load reserve metrics and scale factors to dynamically adjust hardware configuration parameters. This transforms hardware provisioning from a static, conservative approach to a dynamic, data-driven approach that optimizes the balance between reliability and resource efficiency.

Inventive Principle:

Principle #35Parameter changes

3Manufacturing precision

If detailed hardware analysis is performed to determine minimum configuration, then manufacturing precision is improved, but measurement and detection difficulty increases

Engineering Contradiction:

Improvehardware configuration accuracyVSAvoidhardware utilization measurement

Core Design Contradiction:

Manufacturing precisionVSDifficulty of detecting and measuring

Solution Approach 1:

The system introduces intermediary metrics (load reserve, scale factors) that simplify the measurement of complex hardware utilization patterns. These intermediaries translate raw hardware performance data into meaningful indicators that guide hardware configuration decisions, making the measurement process more manageable while maintaining high precision in determining minimum hardware requirements.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The invention changes the measurement approach by focusing on specific key parameters (load reserve, scale factors) rather than attempting to measure all hardware aspects simultaneously. This selective parameter measurement reduces complexity while maintaining accuracy in determining the minimum hardware configuration needed for scaled workloads.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12596493B2Techniques for determining a minimum hardware configuration using performance headroom metrics

Publication Date: 2026.04.07 DELL PROD LP

AI summary

Processing in an embodiment can include: receiving a request for a new hardware configuration of a data storage system, wherein the data storage system has a current configuration and is running a workload W1 with a workload pattern P1 having an associated workload periodicity interval, wherein the new hardware configuration is modeled as running an associated workload W2 with the workload pattern P1 and the associated workload periodicity interval, wherein the workload W2 is equal to the workload W1 multiplied by a target scale factor (TSF); in response to the request, determining the new hardware configuration in accordance with the TSF and a plurality of hardware utilizations of a plurality of hardware components in the current configuration while running the workload W1 with the workload pattern P1 having the associated workload periodicity interval; and upgrading the current configuration of the data storage system based on the new hardware configuration.