Predictive Domain Request Prefetch for Low-Latency DNS Filtering
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Solution Overview
Problem
Current methods for managing domain name requests in Wi-Fi networks are reactive, leading to significant delays and inefficiencies due to the need for real-time domain name categorization and filtering, which is computationally intensive and introduces latency, especially when multiple requests are made in quick succession.
Innovation Solution
Implementing a predictive model in a network management device to anticipate domain name requests, allowing for proactive categorization and filtering by sending anticipated requests to a domain name categorization server for categorization data, thereby reducing latency and improving network efficiency and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time domain name categorization and filtering is implemented, then network security and control are improved, but service latency and performance degradation occur
Solution Approach 1:
The system performs domain name categorization in advance by predicting future domain name requests using a machine learning model. When a domain name request is received, the system checks if it was previously categorized. If categorized, the pre-obtained categorization data is used immediately without real-time processing delay, thus maintaining security while reducing latency.
Solution Approach 2:
The domain name processing workflow is divided into two independent segments: (1) prediction and pre-categorization phase where domain names are predicted and categorized in advance, and (2) request handling phase where requests are processed using pre-obtained categorization data. This segmentation allows categorization operations to be decoupled from request processing, eliminating the latency penalty.
2Reliability
If sophisticated domain name filtering processes are implemented, then network security and control are increased, but performance degradation occurs
Solution Approach 1:
Complex categorization operations are performed in advance during the prediction phase rather than in real-time during request processing. The machine learning model predicts domain names that will be requested, and these predicted domain names are categorized beforehand using sophisticated filtering processes. When actual requests arrive, the pre-computed categorization results are used, maintaining security without performance penalty.
Solution Approach 2:
The system uses a machine learning model to automatically predict which domain names will be requested, eliminating the need for manual configuration of filtering rules. The system self-optimizes by learning from historical request patterns and automatically prioritizing which domain names to pre-categorize, improving both security and performance without human intervention.
Data Source
AI summary
A method and system for managing domain name requests over a communications network, wherein the method involves predicting domain names associated with requests generated by a user device using a model. The model predicts domain names likely to be requested following receipt of an initial domain name request. The method also includes receiving the initial domain name request, processing it to determine predicted domain names, and sending a request including these predicted domain names to a domain name categorization server. A response is received containing categorization data for the initial and predicted domain names. This categorization data is used to filter domain name requests, controlling access to resources associated with these domain names.