1. AI Possibilities for DDI
N3K DDI Roundtable
Andreas Taudte
Principal DDI Consultant
Last updated June 2023
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• Expectations from and Complexity of Infrastructure higher than ever
• Tools intend to automate Actions (“clicking buttons”)
• Intent-based Management required (what, why, when and where)
Why AI …!?
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• Classification Categorize a particular Entity
• Prediction Derive certain Number from continuous Space
• Clustering Extract internal Structure from Data and identify Groups
• Optimization ”Learn” what needs to be done to improve specific Processes
• Generation Generate new or fake Data
Types of Problems
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• Unsupervised Find Knowledge or Structure in Data itself
• Supervised Guide Algorithm with ”Labels” of incoming Data
• Self-supervised Extract “Labels” from Data and use to learn
• Reinforced Generate Experience by making Mistakes
Learning Techniques
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Paradigm Shirt in Programming
Rules
Traditional
Programming
Data
Answers
[Labels]
Machine
Learning
Data
Rules
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• Find non-linear Function f(x) that best connects Data with Labels (answers)
• Determine Information (data) that is truly relevant
Function Approximation
[Labels]
Machine
Learning
Data
Function f(x)
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• Numeric & Continuous/Discrete “ML-friendly” Form Factor
• Categorical & Nominal Text or any other Form Factor required Transformation
• Dictionary Assign Numbers to each Option
• One-Hot-Encoding Transform each categorical Variable into set of binary Variables
Types of Data
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• Population Possible Variation of Parameters & Algorithms
• Fitness Objective Quantification of “fitter”
• Selection Set of Individuals with higher Fitness
• Reproduction Offspring with Combinations of Attributes or Mutations
• Convergence When to stop the Process
Natural Selection Process
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• Classification Classify incoming DNS Queries based on Content and Purpose
• Prediction Analyse historical DHCP Data to anticipate Renewal Request from Clients
• Clustering Group IPs based on Activity for more efficient Troubleshooting
• Optimization Analyse historical DNS Data to optimize Selection of DNS Resolvers
• Generation Generate realistic DNS Query Traffic to simulate different Scenarios
DDI Use Cases: Types of Problems
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• Unsupervised Identify unusual DNS Behaviour indicating potential Misconfigurations
• Supervised Learn from labelled Examples of known malicious DNS Queries
• Self-supervised Learn underlying Patterns and Dependencies in DNS Query Data
• Reinforced Dynamically adjust DHCP Lease Allocation Strategies
DDI Use Cases: Learning Techniques
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• Numeric & Continuous Analyse DNS Query Response Time to make Predictions
• Numeric & Discrete Analyse Number of DHCP Lease Requests to identify Patterns
• Categorical & Nominal Analyse DNS Record Types or DHCP Option Codes to classify Configs
DDI Use Cases: Types of Data
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• Population Maintain Population of DNS Resolver Configurations
• Fitness Evaluate Performance of different DNS Resolver Configurations
• Selection Select DNS Resolver Configurations with highest Query Response Rate
• Reproduction Generate DNS Resolver Configs by combining & modifying existing ones
• Convergence Refine towards Optimum with highest Response Rate or lowest Error Rate
DDI Use Cases: Natural Selection Process
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• Well defined Problem “Snowflakes” with different Problems & large Space of Decisions
• Amount of Data available Frequency of Decisions vs. Data Availability
• Who you gonna call? Domain Expert, Data Scientist, Data Engineer
• Why did it fail? Start with less Complexity to build Trust
Does that even make sense?
