ML-Driven OS Lifecycle Management for Enterprise Networks
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Solution Overview
Problem
Existing OS lifecycle management approaches are prone to errors and performance issues due to the complexity of managing OS upgrades across multiple device types in an enterprise network, often resulting in negative impacts on the computing environment.
Innovation Solution
Implementing a machine learning (ML) driven OS lifecycle management system that automates the entire OS lifecycle, including deployment, monitoring, and upgrade management, using ML models to analyze OS parameters and determine the need for upgrades while minimizing disruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual OS upgrade management is implemented across multiple device types, then OS lifecycle management can be performed, but errors and performance issues increase due to complexity
Solution Approach 1:
The system enables self-service through automated OS lifecycle management where the ML model independently analyzes device parameters, determines upgrade needs, and executes upgrade workflows without manual intervention. This automation eliminates human errors while managing the complexity of multiple device types, directly resolving the contradiction between reliability and management complexity.
Solution Approach 2:
The patent replaces manual mechanical management processes with an intelligent ML-based system. The ML model substitutes human decision-making with automated analysis of OS parameters, device health metrics, and upgrade compatibility, thereby reducing errors while handling the complexity of enterprise-wide OS management across diverse device types.
2Productivity
If automated OS upgrade processes are implemented, then productivity increases, but performance issues may arise due to lack of nuanced decision-making
Solution Approach 1:
The system incorporates feedback mechanisms where the ML model continuously monitors device performance metrics, OS parameters, and upgrade outcomes. This feedback loop enables the model to learn from past upgrades, adjust its decision-making, and prevent performance issues while maintaining high automation efficiency. The feedback-driven approach ensures that productivity gains do not compromise performance stability.
Solution Approach 2:
The ML model dynamically changes operational parameters based on real-time device states, including upgrade timing, resource allocation, and rollback thresholds. By adjusting these parameters adaptively rather than using fixed automated rules, the system maintains high productivity while ensuring performance stability through context-aware decision-making.
3Measurement precision
If comprehensive OS monitoring and analysis is performed across all devices, then upgrade decisions are improved, but system resource consumption increases
Solution Approach 1:
The ML model applies partial monitoring and analysis actions by focusing computational resources only on devices that require OS upgrades or are at risk of performance issues. Rather than continuously analyzing all devices equally, the system performs targeted analysis based on device criticality, upgrade eligibility, and performance thresholds, thereby maintaining measurement precision while reducing overall resource consumption.
Data Source
AI summary
A system includes a memory, and a processing device, operatively coupled to the memory, to perform operations including obtaining, by at least one processing device, input data comprising a set of parameters associated with a computing environment, determining, by the at least one processing device using a machine learning (ML) model based on the input data, whether a device of the computing environment is due for an operating system (OS) upgrade, and in response to determining that the device is due for the OS upgrade, initiating, by the at least one processing device, the OS upgrade for the device.