ML-Based Content Delivery Routing and Caching

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Solution Overview

Problem

Current content delivery systems face challenges in optimizing user experience and business efficiency due to varying network conditions, computing capabilities, and caching strategies, which affect latency and customer retention.

Innovation Solution

Implementing machine learning techniques for cache management and content delivery strategies, including clustering content requests to route them to optimal servers, pre-caching frequently requested content, and dynamically determining content delivery strategies based on historical data and user interactions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of time

If content is cached at CDN service provider computing devices, then content delivery latency is reduced, but cache management complexity increases

Engineering Contradiction:

Improvecontent delivery latencyVSAvoidcache management complexity

Core Design Contradiction:

Loss of timeVSDevice complexity

Solution Approach 1:

The patent replaces manual or rule-based cache management with machine learning models that automatically predict content delivery performance and optimize caching strategies. The ML models analyze historical data, user behavior, and network conditions to make intelligent caching decisions, substituting complex mechanical cache management processes with automated intelligent systems.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent dynamically adjusts cache management parameters such as cache size, eviction policies, and pre-fetching strategies based on predicted content delivery performance. The system changes caching parameters in response to varying network conditions, user behavior patterns, and content characteristics, optimizing the balance between latency reduction and resource utilization.

Inventive Principle:

Principle #35Parameter changes

2Reliability

If content delivery strategies are optimized for specific user conditions, then user experience is improved, but system adaptability requirements increase

Engineering Contradiction:

Improveuser experience consistencyVSAvoidsystem adaptability to varying conditions

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The patent implements dynamic content delivery strategies that adapt to real-time user conditions, network state, and content characteristics. The system continuously adjusts delivery parameters such as content format, quality, and routing based on predicted performance metrics, enabling flexible adaptation to varying conditions while maintaining consistent user experience quality.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system uses machine learning models that incorporate feedback from actual content delivery performance, user behavior data, and network conditions to continuously optimize delivery strategies. The feedback loop enables the system to learn from past performance and improve future content delivery decisions, balancing adaptability with experience consistency.

Inventive Principle:

Principle #23Feedback

3Productivity

If machine learning models are used to predict content delivery performance, then content delivery optimization is improved, but computational requirements increase

Engineering Contradiction:

Improvecontent delivery optimizationVSAvoidcomputational energy consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The patent employs machine learning models to predict content delivery performance in advance, enabling proactive optimization of content delivery strategies. The models analyze historical data and current conditions to forecast performance metrics before actual content delivery occurs, allowing the system to pre-adjust caching, routing, and formatting decisions to optimize delivery efficiency.

Inventive Principle:

Principle #10Preliminary action

4Reliability

If content requests are routed to optimal servers based on clustering, then cache hit rate is improved, but request routing complexity increases

Engineering Contradiction:

Improvecache hit rateVSAvoidrequest routing complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent segments content requests into different clusters based on characteristics such as user behavior, content type, and network conditions. By dividing the request space into meaningful segments, the system can apply optimized routing strategies to each cluster, improving cache hit rates while managing routing complexity through structured categorization rather than monolithic decision-making.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS10311372B1Machine learning based content delivery

Publication Date: 2019.06.04 AMAZON TECH INC

AI summary

Systems and methods for managing content delivery functionalities based on machine learning models are provided. In one aspect, content requests are routed in accordance with clusters of historical content requests to optimize cache performance. In another aspect, content delivery strategies for responding to content requests are determined based on a model trained on data related to historical content requests. The model may also be used to determine above-the-fold configurations for rendering responses to content requests. In some embodiments, portions of the model can be executed on client computing devices.