Workload Shutdown Scheduling for Off-Peak Resource Efficiency

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Solution Overview

Problem

Existing distributed computing environments face inefficiencies in resource utilization due to workloads using fewer resources during off-peak times, leading to potential cost wastage and inefficiencies.

Innovation Solution

Implementing automated shutdown and restart management sequences that include backup, shutdown, and restart protocols, allowing workloads to be shut down during off-peak times with exemptions for critical workloads, ensuring efficient resource use and data integrity.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If workloads are continuously running in distributed computing environment, then service availability is maintained, but resource utilization efficiency deteriorates during off-peak times

Engineering Contradiction:

Improveservice availabilityVSAvoidresource utilization efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system implements periodic shutdown and restart of workloads based on predicted demand patterns. Workloads are shut down during off-peak periods and restarted before peak periods begin, achieving resource conservation while maintaining service availability when needed

Inventive Principle:

Principle #19Periodic action

Solution Approach 2:

The system performs preliminary backup of workload data before shutdown and preliminary restart preparation before peak demand. This ensures that workloads can be quickly restored when needed, maintaining service availability while enabling shutdown during low-utilization periods

Inventive Principle:

Principle #10Preliminary action

2Loss of energy

If workloads are shut down during off-peak times, then resource consumption is reduced, but service availability deteriorates

Engineering Contradiction:

Improveresource consumptionVSAvoidservice availability

Core Design Contradiction:

Loss of energyVSReliability

Solution Approach 1:

The system uses demand prediction feedback to dynamically control workload shutdown and restart timing. By continuously monitoring and predicting demand patterns, the system shuts down workloads when demand is low and restarts them before demand increases, optimizing both resource consumption and service availability

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system creates backup copies of workload data before shutdown. This copying mechanism enables rapid restoration of workloads when needed, ensuring service availability is maintained even during shutdown periods for resource conservation

Inventive Principle:

Principle #26Copying

3Productivity

If automated shutdown and restart sequences are implemented, then resource efficiency is improved, but system complexity increases

Engineering Contradiction:

Improveresource efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system implements self-service automation where the workload management system automatically performs backup, shutdown, and restart operations based on predicted demand. This eliminates manual intervention complexity while achieving resource efficiency through automated decision-making and execution

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20260064540A1Shutdown and restart management in a distributed computing environment

Publication Date: 2026.03.05 HONEYWELL INTERNATIONAL INC

AI summary

Devices, methods, and systems for shutdown and restart management in a distributed computing environment are described herein. One method includes determining whether a backup of a workload in a distributed computing environment exists, causing a shutdown sequence of the workload to occur at a first predetermined time to shut down the workload, and causing a restart sequence of the workload to occur to restart the workload in the distributed computing environment.