Network Service Configuration via Hidden Markov Models
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Solution Overview
Problem
Network administrators face challenges in creating effective network service configurations due to broad guidelines and manual processes, leading to user errors and resource wastage.
Innovation Solution
A service management platform utilizing machine learning, specifically Hidden Markov Models, to analyze telemetry data and recommend optimized network service configurations, automating the process and reducing human subjectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processes are used to create network service configurations based on broad guidelines, then network administrators have flexibility in decision-making, but user errors increase and productivity decreases
Solution Approach 1:
The system enables self-service by automatically generating network service configurations through machine learning models that analyze telemetry data and service requirements, eliminating the need for manual configuration creation while ensuring accuracy through automated validation processes
Solution Approach 2:
The patent replaces the mechanical manual process of configuration creation with an automated machine learning system that uses Hidden Markov Models and other ML techniques to generate configurations, thereby improving both reliability and productivity simultaneously
2Reliability
If manual configuration creation processes are used, then network administrators can apply domain knowledge, but resource wastage increases due to errors and rework
Solution Approach 1:
The system performs preliminary action by pre-generating optimized configurations through machine learning models before deployment, validating configurations in advance, and preventing errors before they consume computational resources in production environments
Solution Approach 2:
The patent implements feedback mechanisms where telemetry data from live networks continuously trains and improves the machine learning models, creating a closed-loop system that learns from past configurations and outcomes to progressively reduce resource wastage while maintaining correctness
3Productivity
If automated machine learning systems are used to generate network service configurations, then productivity increases and resource efficiency improves, but system complexity increases
Solution Approach 1:
The system achieves universality by creating a multi-functional platform that handles configuration generation, validation, optimization, and deployment through integrated machine learning models, reducing the need for separate specialized systems while maintaining high productivity
Solution Approach 2:
The patent uses intermediary components such as feature extraction layers, model training pipelines, and configuration validation intermediaries that bridge the gap between complex machine learning algorithms and simple configuration outputs, managing system complexity while preserving productivity benefits
Data Source
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AI summary
A device may receive a request for a network service configuration (NSC) that is to be used to configure network devices. The device may select a graphical data model that has been trained via machine learning to analyze a dataset that includes information relating to a set of network configuration services, where aspects of a subset of the set of network configuration services have been created over time. The device may determine, by using the graphical data model, a path through a set of states of the graphical data model, where the path corresponds to a particular NSC. The device may select the particular NSC based on the path determined. The device may perform a first group of actions to provide data identifying the particular NSC for display, and/or a second group of actions to implement the particular NSC on the network devices.