AI-Supported CDN Engine Automates Issue Resolution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing content distribution networks (CDNs) face challenges in identifying and resolving issues efficiently, especially as the number of servers and devices increases, relying on cross-team communication and specialized knowledge.
Innovation Solution
The implementation of an AI-supported network within a CDN using artificial intelligence (AI) and machine learning techniques to process log data, identify issues, generate solutions, and automatically implement those solutions through specialized engines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual issue identification and resolution methods are used in a CDN, then specialized knowledge and cross-team communication are required, but the process becomes inefficient and difficult to scale as the number of servers increases
Solution Approach 1:
The system implements self-service through automated issue identification and resolution. The data processing engine automatically analyzes log data to identify issues, and the solution implementation engine automatically applies fixes without requiring human intervention or cross-team communication, enabling the CDN to resolve its own issues efficiently
Solution Approach 2:
The patent replaces manual mechanical processes with automated computational systems. Instead of human operators manually analyzing logs and implementing fixes, machine learning models and automated engines perform these tasks, substituting human expertise with algorithmic processing that scales efficiently
2Reliability
If specialized knowledge is required for issue resolution, then expert teams can handle complex problems, but the process slows down and becomes dependent on human availability
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models with historical issue data and resolution patterns. This allows the system to have expert knowledge pre-loaded and ready, enabling immediate issue resolution without waiting for human experts to become available
Solution Approach 2:
The patent introduces an intermediary layer of machine learning models and automated engines between the log data and the resolution process. This intermediary automatically translates raw logs into actionable insights and applies fixes, eliminating the need for direct human expert intervention while maintaining high reliability
3Measurement precision
If manual monitoring and analysis of log data is performed, then detailed issue detection is possible, but the process is time-consuming and cannot keep pace with increasing network complexity
Solution Approach 1:
The patent replaces manual data analysis with automated computational processing. Machine learning models process log data at machine speed, achieving both high precision in issue detection and high productivity in processing volume, eliminating the trade-off present in manual analysis
Solution Approach 2:
The system changes the parameters of data processing by using machine learning models that can analyze multiple log entries simultaneously and identify patterns across vast datasets. This enables both precise issue detection and rapid processing by transforming how the data is analyzed rather than just increasing processing power
Data Source
AI summary
Examples of the present disclosure relate to an AI-supported CDN. In examples, a data processing engine processes log data of a CDN node according to a model to identify an issue. An issue indication is provided to a solution generation engine, which generates a set of solutions to automatically resolve the issue. The set of solutions is provided to a solution implementation engine, which iteratively implements solutions to resolve the issue using solution implementation information associated with a given solution. Thus, the data processing engine need not have knowledge regarding the specific hardware and/or software used within the CDN. Similarly, the solution generation engine need not have knowledge of the structure of the CDN and/or configuration of devices associated with the identified issue, such that the solution implementation engine provides a layer of abstraction between a solution and the implementation-specific details used to implement the solution within the CDN.