Outage Window Scheduler Tool for Server Maintenance

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Solution Overview

Problem

Current methods for scheduling maintenance and upgrades in computer systems often rely on guesswork, leading to inconveniences or disruptions during high utilization periods, as they lack a reliable way to determine optimal downtime windows.

Innovation Solution

An outage window scheduler tool that analyzes historical and scheduled utilization data using statistical methods to predict and recommend future time windows with low processor utilization, assigning a confidence level to each window to minimize impact on the system.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If maintenance activities are scheduled during predictable low-use periods (e.g., nighttime), then maintenance can be performed with minimal user impact, but this approach may still result in scheduling during high utilization periods when usage patterns are unpredictable

Engineering Contradiction:

Improveaccuracy of outage window selectionVSAvoidtime spent on guesswork and manual scheduling

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs self-analysis by automatically collecting historical utilization data, analyzing patterns, and generating outage window recommendations without requiring external expert intervention. The computer system serves itself by using its own operational data to determine optimal maintenance timing.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system continuously monitors historical utilization data and uses this feedback to improve future outage window predictions. By analyzing past usage patterns and adjusting recommendations based on observed trends, the system refines its accuracy over time.

Inventive Principle:

Principle #23Feedback

2Device complexity

If human experts use educated guesswork to determine future outage windows, then scheduling can be performed without complex tools, but the accuracy and reliability of outage window selection deteriorates

Engineering Contradiction:

Improvesimplicity of scheduling toolVSAvoidprecision of utilization pattern analysis

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent replaces the mechanical process of human guesswork with an automated computational system. Instead of relying on human intuition and experience, the system uses algorithms to analyze historical data and predict optimal outage windows, substituting human cognitive processes with automated mechanical computation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system introduces an intermediary automated analysis layer between raw utilization data and scheduling decisions. This intermediary process objectively processes historical data and generates recommendations, removing the need for direct human judgment while maintaining scheduling simplicity through automated report generation.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Reliability

If statistical analysis and historical data are used to predict future outage windows, then the reliability of maintenance scheduling improves, but the complexity of the scheduling system increases

Engineering Contradiction:

Improveconfidence level of outage window predictionVSAvoidcomplexity of data analysis and prediction system

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system segments the complex scheduling problem into distinct analytical components: data collection, pattern recognition, prediction generation, and recommendation delivery. By dividing the overall process into manageable segments, the system reduces perceived complexity while maintaining high predictive reliability through systematic analysis.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs multiple functions within a unified platform: it collects utilization data, analyzes historical patterns, generates predictions, and presents recommendations. This multi-functional approach consolidates what would otherwise require separate tools and processes, managing complexity through integration rather than proliferation of components.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS9372734B2Outage window scheduler tool

Publication Date: 2016.06.21 BANK OF AMERICA CORP

AI summary

An apparatus for determining a future outage window in which to perform work on a server may include an input for receiving historical performance data about at least one server, a non-transitory memory and a processor communicatively coupled to the input and the memory. The processor may be configured to use instructions stored in the memory to predict one or more likely future time windows to be recommended as possible outage windows in which to perform work on the server. The processor may analyze historical performance data about the at least one server to determine one or more historical time windows corresponding to low CPU utilization of the at least one server. In some cases, the processor may then predict one or more future time windows to be recommended for use as the outage window using, at least in part, the determined historical time windows.