AI Deployment Policy for Ring Rollout and Bake Time Decisions

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Solution Overview

Problem

Developers often make non-optimal choices in deciding rollout policies, bake times, and deployment times for software updates, as there are numerous variables and user considerations that affect the optimal deployment strategy.

Innovation Solution

A system utilizing artificial intelligence (AI) to recommend customized optimal ring policies, bake times, and deployment times based on factors such as user risk events, peak usage times, software change risk, rollout history, and compliance assessment history, with continuous learning from deployment feedback to refine recommendations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If developers manually decide rollout policies, bake times, and deployment times, then deployment control and flexibility are maintained, but deployment optimization and reliability are compromised due to non-optimal choices

Engineering Contradiction:

Improvedeployment reliabilityVSAvoiddeployment decision complexity

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system enables self-service deployment optimization by automatically analyzing deployment data, identifying patterns, and generating optimized rollout policies, bake times, and deployment time recommendations without requiring manual developer intervention for each decision

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual developer decision-making (mechanical human process) with an automated system that uses machine learning models and data analysis to generate deployment recommendations, substituting human judgment with algorithmic optimization

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If ring-based deployment is implemented to reduce risk, then service reliability is improved, but deployment time and complexity increase due to phased rollouts across multiple rings

Engineering Contradiction:

Improveservice reliabilityVSAvoiddeployment time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system dynamically adjusts ring promotion criteria and deployment timing based on real-time monitoring of service metrics, allowing accelerated progression through rings when conditions are favorable while maintaining reliability safeguards, thus reducing overall deployment time without compromising service stability

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent optimizes deployment parameters such as ring size, promotion thresholds, and timing intervals based on historical data and service characteristics, allowing customization of the ring-based deployment process to balance reliability and speed for different deployment scenarios

Inventive Principle:

Principle #35Parameter changes

3Stability of the object's composition

If extensive monitoring and bake times are used to ensure stability, then service stability is improved, but deployment speed and productivity are reduced

Engineering Contradiction:

Improveservice stabilityVSAvoiddeployment speed

Core Design Contradiction:

Stability of the object's compositionVSProductivity

Solution Approach 1:

The system implements continuous feedback loops that monitor service metrics during and after deployments, using this data to dynamically adjust monitoring intensity and bake time requirements, reducing unnecessary waiting periods while maintaining stability through targeted, data-driven observation

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent applies monitoring and bake times selectively based on deployment risk assessment, using lighter monitoring for low-risk changes and more extensive monitoring only when necessary, thus avoiding excessive action that would slow down routine deployments while maintaining stability for critical changes

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20250377876A1Deployment policy for software updates across cloud environments driven by artificial intelligence

Publication Date: 2025.12.11 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

A data processing system includes a processor and a memory for the processor. The memory stores executable instructions that, when executed by the processor alone or in combination with other processors, cause the data processing system to perform functions of: receive a deployment request to deploy a software change; determine factors corresponding to the deployment request that impact an optimal deployment policy for the software change; query an Artificial Intelligence (AI) trained with a dataset of optimized deployment policies based on corresponding sets of the factors, the query requesting an optimized deployment policy for the software change of the received deployment request based on the determined factors and including a ring rollout policy, ring bake time and deployment time; execute the deployment request using the optimized deployment policy returned by the AI; and update training of the AI based on the determined factors and results of the optimized deployment policy.