Cloud Change Management Meta Model Optimization

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Solution Overview

Problem

Conventional cloud management platforms face challenges in efficiently managing multi-tenant cloud environments due to complexities in scheduling changes across diverse geographical areas and time zones, leading to time-consuming and error-prone processes, while also needing to minimize service disruption during maintenance windows.

Innovation Solution

A change management system that utilizes machine learning to build a change management meta model based on interdependencies and constraints, producing a change request fulfillment plan that optimizes the maintenance window by parallel processing of change tasks, thereby minimizing downtime and service disruption.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional cloud management platforms schedule changes manually across multi-tenant environments, then service disruption can be managed within agreed time windows, but the scheduling process becomes time-consuming and error-prone

Engineering Contradiction:

Improveservice disruption managementVSAvoidscheduling process time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical scheduling processes with an automated machine learning-based system. The ML model automatically analyzes change requests, identifies dependencies, and generates optimized fulfillment plans, eliminating the time-consuming manual scheduling while maintaining service disruption management within agreed time windows.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces a change management meta-model as an intermediary layer between change requests and fulfillment execution. This meta-model captures dependencies and constraints, enabling automated reasoning and optimization of change schedules without manual intervention, thus reducing scheduling time while ensuring reliable service disruption management.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Stability of the object's composition

If change tasks are processed sequentially to ensure proper dependency management, then service stability is maintained, but maintenance window duration increases

Engineering Contradiction:

Improveservice stabilityVSAvoidmaintenance window duration

Core Design Contradiction:

Stability of the object's compositionVSDuration of action of moving object

Solution Approach 1:

The patent dynamically determines the execution order of change tasks based on their dependencies and constraints. The ML-based optimizer analyzes the change management meta-model to identify which tasks can be safely executed in parallel and which must be sequential, thereby minimizing maintenance window duration while maintaining service stability through proper dependency management.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent transforms the traditional single-threaded sequential change execution into a multi-dimensional parallel execution model. By analyzing dependencies across multiple dimensions (task-level, component-level, service-level), the system identifies independent task groups that can be executed concurrently, reducing overall maintenance window duration while preserving service stability through controlled parallelism.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

3Productivity

If automated deployment is implemented to reduce manual errors, then deployment speed increases, but complexity in managing interdependencies between change tasks and deployment processes increases

Engineering Contradiction:

Improvedeployment speedVSAvoidinterdependency management complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent segments the complex interdependency management problem into distinct components: change request tasks, deployment processes, and their relationships. The change management meta-model separately captures task dependencies, deployment dependencies, and task-deployment mappings, making the overall system more manageable and easier to automate while maintaining high deployment speed.

Inventive Principle:

Principle #1Segmentation

4Loss of time

If maintenance window is minimized to reduce service disruption, then customer satisfaction improves, but the complexity of coordinating multiple change tasks within the constrained time window increases

Engineering Contradiction:

Improveservice disruption timeVSAvoidcoordination complexity

Core Design Contradiction:

Loss of timeVSDevice complexity

Solution Approach 1:

The patent performs preliminary analysis and planning of change task coordination before execution. The ML-based optimizer pre-processes change requests, builds the change management meta-model, and generates an optimized fulfillment plan that coordinates all tasks within the minimized maintenance window. This preliminary action reduces the complexity of real-time coordination during actual execution.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11018955B2Change management optimization in cloud environment

Publication Date: 2021.05.25 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Methods, computer program products, and systems are presented. The methods include, for instance: building a change management meta model on relationships between change request tasks of a change request and logical components of a computing environment via deployment processes that realizes respective change request tasks onto the logical components. A change request fulfillment plan that minimizes a maintenance window for deploying the change request tasks to the logical components is produced. After deployment, performance metrics of the logical components updates change request constraints.