AI-Driven CDN Scheduling for Load Balancing and Hotspot Distribution
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Solution Overview
Problem
Existing CDN systems face challenges in operation and maintenance efficiency due to manual scheduling methods, lack of effective measurement and evaluation systems, unbalanced load distribution, and unbalanced hotspot service, which hinder their ability to cope with the demands of modern internet and 5G technologies.
Innovation Solution
Implementing an AI-driven CDN scheduling system that generates real-time index systems for CDN and metropolitan area networks, using AI training and optimization to create intra-region and inter-region scheduling algorithms, and executing a CDN scheduling policy to balance load and distribute hotspots intelligently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual operation and maintenance mode is used, then simplicity of implementation is maintained, but operation and maintenance efficiency deteriorates
Solution Approach 1:
The CDN system implements self-service through automated monitoring agents deployed at node devices that automatically collect data, generate hotspot statistics, and trigger scheduling decisions without manual intervention. The system autonomously performs operation and maintenance tasks including load balancing and content distribution optimization.
Solution Approach 2:
The patent replaces manual mechanical operation with an intelligent automated system comprising monitoring agents, data processing modules, and AI-based scheduling algorithms. This substitution transforms the manual operation and maintenance process into an automated intelligent system that continuously optimizes CDN performance.
2Measurement precision
If statistical report and alarm detection are used, then implementation simplicity is maintained, but service quality monitoring capability deteriorates
Solution Approach 1:
The system implements comprehensive feedback mechanisms where monitoring agents continuously collect service quality data from CDN node devices, process this data through multiple modules (hotspot statistics, service quality assessment, scheduling optimization), and use the feedback to dynamically adjust content distribution and load balancing strategies.
Solution Approach 2:
The patent introduces intermediary components including monitoring agents that collect data from node devices, data processing modules that analyze service quality metrics, and scheduling modules that translate analysis results into optimization actions. These intermediaries enable precise service quality measurement and control.
3Reliability
If conventional scheduling methods are used, then system simplicity is maintained, but load balancing performance deteriorates
Solution Approach 1:
The system performs preliminary actions by proactively identifying hotspots through continuous monitoring and statistical analysis before they cause load imbalances. The scheduling module pre-adjusts content distribution based on predicted traffic patterns and service quality requirements, preventing load balancing issues before they occur.
Solution Approach 2:
The patent implements dynamic scheduling that continuously adapts to changing traffic patterns and service quality requirements. The system dynamically adjusts content distribution strategies, load balancing parameters, and resource allocation based on real-time monitoring data and AI-based optimization algorithms.
4Ease of operation
If manual hotspot distribution is used, then implementation simplicity is maintained, but hotspot service balance deteriorates
Solution Approach 1:
The system implements self-service for hotspot distribution through automated monitoring agents that independently identify hotspots, analyze their characteristics, and trigger appropriate distribution strategies without manual intervention. The scheduling module automatically adjusts content placement based on detected hotspot patterns and service quality requirements.
Solution Approach 2:
The patent replaces manual hotspot identification and distribution with an intelligent automated system comprising monitoring agents that detect hotspots, data processing modules that analyze hotspot characteristics, and scheduling algorithms that optimize content distribution. This substitution significantly improves both operational efficiency and service quality.
Data Source
AI summary
Embodiments of the present disclosure provide a method, system and device for content delivery network (CDN) scheduling, and a storage medium. The method includes: acquiring CDN data in real time from a CDN node device to generate a CDN index system; acquiring metropolitan area network, MAN, data in real time from a MAN to generate a MAN index system; generating a CDN node load intelligent image based on the CDN index system, and generating an intra-region scheduling algorithm through artificial intelligence, AI, training and algorithm optimization; generating a CDN region load intelligent image based on the CDN index system and the MAN index system, and generating an inter-region scheduling algorithm through the AI training and the algorithm optimization; and determining a CDN scheduling policy according to the intra-region scheduling algorithm and the inter-region scheduling algorithm, and executing the CDN scheduling policy.