Program Code Update Scheduling Using Multivariate Time Series Analysis

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Solution Overview

Problem

Updating program code across a large number of distributed computing devices in information processing systems is challenging due to the manual or semi-automated nature of existing methods, which can cause disruptions and are inefficient, especially when dealing with edge devices across different time zones.

Innovation Solution

A multi-variate time series model is used to analyze resource utilization data and automatically compute an optimal time window for program code updates, minimizing disruptions by scheduling updates during periods of low workload, such as when CPU, IO, and disk utilization are low.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual or semi-automated methods are used to update program code on distributed computing devices, then implementation simplicity is maintained, but update efficiency and reliability deteriorate due to disruptions and manual intervention requirements

Engineering Contradiction:

Improveupdate efficiencyVSAvoidupdate management complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system automatically schedules and executes program code updates on distributed computing devices without requiring manual intervention. The update management system autonomously selects optimal time windows, pushes updates to devices, and monitors completion, enabling the system to self-manage the update process and eliminate manual operational complexity

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary analysis of device status, workload patterns, and update requirements before scheduling updates. By pre-evaluating conditions and preparing update packages in advance, the system can execute updates at optimal moments without disrupting device operations, thereby improving efficiency while maintaining simplicity

Inventive Principle:

Principle #10Preliminary action

2Reliability

If program code updates are performed during high workload periods, then update speed increases, but device reliability and operational stability deteriorate due to disruptions

Engineering Contradiction:

Improvedevice reliabilityVSAvoidupdate time window

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system dynamically adjusts update scheduling based on real-time device status and workload conditions. By continuously monitoring device metrics and adapting the update timing to match low-workload periods, the system maintains high reliability while minimizing the time window required for updates through flexible, condition-based scheduling

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system incorporates feedback loops that monitor device responses to updates and workload patterns. This feedback enables the system to learn from previous update outcomes and refine future scheduling decisions, ensuring updates are always performed during optimal low-disruption periods, thereby improving reliability without significant time loss

Inventive Principle:

Principle #23Feedback

3Extent of automation

If automated update scheduling is implemented without considering workload patterns, then automation level increases, but update disruption and harmful effects increase due to poor timing

Engineering Contradiction:

Improveupdate automation levelVSAvoidupdate disruption

Core Design Contradiction:

Extent of automationVSObject-affected harmful factors

Solution Approach 1:

The system replaces simple mechanical scheduling with intelligent automation that incorporates machine learning models and analytical algorithms. This substitution enables the system to automatically determine optimal update timing based on complex workload patterns, device metrics, and historical data, achieving high automation while eliminating disruption through sophisticated decision-making

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system changes the parameters of update scheduling by analyzing multiple device metrics and workload characteristics to determine optimal timing conditions. By adjusting parameters such as update timing, duration, and frequency based on real-time conditions, the system achieves automated scheduling that avoids disruptions while maintaining high automation levels

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250004747A1Device program code management in information processing system environment

Publication Date: 2025.01.02 DELL PROD LP

AI summary

Techniques for program code management are disclosed. For example, a method obtains resource utilization data from a computing network comprising a plurality of computing devices. The method then utilizes a multi-variate time series model representing at least a portion of the resource utilization data to automatically compute at least one time window in which to perform a program code update on at least a subset of the plurality of computing devices.