Predictive Content Push CDN Using AI Models
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Solution Overview
Problem
Content delivery networks (CDNs) face delays in delivering content resources due to factors like network congestion, user device capabilities, and inaccurate prediction of content requests, leading to inefficient resource utilization and increased latency in rendering content pages.
Innovation Solution
Implementing a predictive content push system that uses artificial intelligence models, such as Markov models and neural networks, to anticipate user content requests based on historical data and real-time conditions, proactively transmitting relevant resources to user devices before they are explicitly requested.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If CDN uses traditional request-response content delivery model, then system complexity is low, but content delivery latency increases and resource utilization becomes inefficient
Solution Approach 1:
The system performs preliminary actions by using AI models to predict future content requests and proactively pushing content to edge devices before users actually need it. This anticipatory approach eliminates the waiting time inherent in traditional request-response models, directly addressing the latency issue while accepting the necessary increase in system complexity for achieving superior performance.
2Measurement precision
If CDN implements predictive content push using AI models, then content delivery accuracy improves, but energy consumption increases
Solution Approach 1:
The system applies local quality by deploying AI models at edge locations rather than requiring all processing to occur at centralized data centers. This distributes the computational workload more efficiently, reducing overall energy consumption while maintaining high prediction accuracy through localized intelligent content delivery decisions.
3Reliability
If CDN pushes more content proactively, then content availability improves, but network bandwidth consumption increases
Solution Approach 1:
The system utilizes parameter changes by dynamically adjusting push parameters such as content type, quantity, and timing based on real-time network conditions and user behavior patterns. This optimization ensures content is pushed only when and where needed, improving availability while controlling bandwidth consumption through adaptive parameter modification.
Data Source
AI summary
A content delivery network (“CDN”) is provided herein that predicts content resources (e.g., a data object, such as a video file, an audio file, a script, an image, a document, etc.) that may be requested by a user device in the future and transmits or pushes such resources to the user device prior to receiving a request. The CDN may use artificial intelligence models, such as Markov models, in order to predict which content resources to retrieve and transmit proactively to the user device. The predictive techniques implemented by the CDN may reduce a latency of delivering requested content resources and/or a latency of the user device in rendering and displaying a content page.