Intelligent Change Window Planning for Low-Impact Network Updates
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Solution Overview
Problem
Current methods for determining change windows in enterprise networks are inconvenient, often requiring manual identification by administrators and are not always accurate, especially when considering change window type, urgency, and user preferences.
Innovation Solution
An intelligent change window planner system that utilizes AI-enhanced RRM and machine learning to automatically determine windows of least impact, taking into account change window type, urgency, and user preferences, while identifying associated risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If change windows are scheduled during weekends, holidays, or late at night, then network disruption is minimized, but administrator convenience and operational efficiency deteriorate
Solution Approach 1:
The system enables self-service by automatically identifying and scheduling change windows without requiring administrator intervention. The intelligent planner autonomously analyzes network conditions, predicts optimal time slots, and schedules changes during periods of minimal impact, eliminating the need for administrators to manually coordinate with users or guess appropriate timing.
Solution Approach 2:
The system dynamically adjusts scheduling parameters based on real-time network conditions, user activity patterns, and change urgency. By continuously monitoring and adapting to changing parameters such as network traffic volume, user login patterns, and application usage, the system identifies optimal windows that minimize disruption while accommodating administrator constraints.
2Adaptability or versatility
If change windows are scheduled manually by administrators, then flexibility in considering user preferences is improved, but time consumption and scheduling accuracy deteriorate
Solution Approach 1:
The system implements feedback loops that continuously collect data on user preferences, network performance, and change outcomes. This feedback is processed by machine learning algorithms that refine scheduling predictions over time, automatically incorporating user preferences and behavioral patterns to improve future scheduling decisions without requiring additional administrator time.
Solution Approach 2:
The patent replaces the manual mechanical process of administrator scheduling with an automated intelligent system. Machine learning models and AI algorithms substitute for human analysis, automatically processing user preference data, network metrics, and change requirements to generate optimized schedules, thereby eliminating time-consuming manual guesswork while maintaining adaptability.
3Ease of manufacture
If traditional change window scheduling is used, then simplicity of implementation is maintained, but scheduling accuracy and disruption prediction deteriorate
Solution Approach 1:
The system performs preliminary analysis of network conditions, user patterns, and change characteristics before scheduling. By pre-processing data and predicting potential disruption scenarios in advance, the system identifies optimal time slots with high confidence, improving prediction accuracy while maintaining ease of implementation through automated preparatory steps.
Data Source
AI summary
This disclosure describes techniques and mechanisms for determine a change window of least impact based on the type of activity, urgency, and preference, and highlighting risk(s) of choosing a change window. The techniques streamline and automate change window technology and provide customized and personalized change window option(s) to an administrator of a network.