Hardware Usage Prediction via Regression Analysis

Solution Overview

Problem

Existing methods for predicting hardware usage in computing systems often fail to accurately account for cumulative factors, leading to either under-provisioning of resources, resulting in suboptimal user experiences or over-provisioning, causing unnecessary capital expenditures, especially in large-scale systems.

Innovation Solution

A regression analysis-based model that combines historical rate and cumulative factors to generate a hardware usage function, which can accurately project future hardware needs, ensuring adequate resource allocation while minimizing unnecessary spending.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If hardware resources are over-provisioned to ensure adequate resources, then reliability of service is improved, but capital expenditure increases

Engineering Contradiction:

Improveservice reliabilityVSAvoidcapital expenditure

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The system performs preliminary analysis of historical usage data and cumulative factors before making hardware provisioning decisions. By using regression analysis on past data patterns, the system predicts future hardware needs in advance, allowing organizations to provision resources proactively rather than reactively, thus avoiding both over-provisioning and under-provisioning

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system establishes a feedback loop where hardware usage data is continuously collected, analyzed against cumulative factors, and used to update predictions. This feedback mechanism allows the system to learn from actual usage patterns and adjust future provisioning recommendations, improving accuracy over time and reducing unnecessary capital expenditures

Inventive Principle:

Principle #23Feedback

2Quantity of substance

If hardware resources are under-provisioned to reduce capital expenditure, then capital expenditure is reduced, but user experience deteriorates

Engineering Contradiction:

Improvecapital expenditureVSAvoiduser experience

Core Design Contradiction:

Quantity of substanceVSEase of operation

Solution Approach 1:

The system performs preliminary analysis of historical usage data and cumulative factors before making hardware provisioning decisions. By using regression analysis on past data patterns, the system predicts future hardware needs in advance, allowing organizations to provision resources proactively rather than reactively, thus avoiding both over-provisioning and under-provisioning

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system establishes a feedback loop where hardware usage data is continuously collected, analyzed against cumulative factors, and used to update predictions. This feedback mechanism allows the system to learn from actual usage patterns and adjust future provisioning recommendations, improving accuracy over time and reducing unnecessary capital expenditures

Inventive Principle:

Principle #23Feedback

3Device complexity

If traditional prediction methods are used that ignore cumulative factors, then device complexity is reduced, but measurement precision of hardware usage deteriorates

Engineering Contradiction:

Improveprediction model complexityVSAvoidhardware usage prediction accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The prediction model is segmented into distinct components: rate factors (current usage velocity) and cumulative factors (historical accumulation). This segmentation allows the system to analyze different aspects of hardware usage separately and combine them through regression analysis, improving prediction accuracy without creating an monolithic complex system

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system changes the parameters used in prediction by introducing cumulative factors alongside traditional rate factors. This parameter expansion transforms the prediction model from considering only current usage velocity to also incorporating historical accumulation, thereby improving measurement precision while maintaining manageable complexity through systematic analysis

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS8417811B1Predicting hardware usage in a computing system

Publication Date: 2013.04.09 AMAZON TECH INC

AI summary

Disclosed are various embodiments of modeling hardware usage in a computing system. Usage factors affecting hardware usage of the computing system are identified. Usage factor data over a time period that corresponds to the usage factors is extracted. A regression analysis is performed on the historical usage factor data that determines a hardware usage function characterizing a relationship between the usage factors and historical hardware usage data.