CDN Optimization Platform Using Bayesian Traffic Routing
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Solution Overview
Problem
In a fusion or hybrid CDN model, efficiently distributing user data traffic among multiple providers to minimize cost is challenging due to the complexity of managing multiple service providers as a collective resource pool.
Innovation Solution
Implementing a data and analysis platform with a machine learning algorithm and a smart routing engine to optimize traffic distribution. This platform integrates multiple data sources for network traffic data and provides a user interface for reporting cost and performance metrics, enabling real-time optimization of traffic distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fusion or hybrid CDN model is used to dynamically route traffic among multiple providers, then service availability and performance are improved, but the complexity of managing multiple service providers as a collective resource pool increases
Solution Approach 1:
The patent segments the complex CDN management system into distinct functional modules: a metrics collection system that gathers performance data from multiple CDN providers, a simulation system that models traffic distribution scenarios, and an optimization system that determines optimal routing decisions. This segmentation allows each module to handle specific aspects of management complexity independently while working together to improve service availability.
Solution Approach 2:
The patent introduces a simulation system as an intermediary between raw metrics data and traffic distribution decisions. This intermediary layer processes and contextualizes performance metrics from multiple providers, enabling the optimization system to make informed routing decisions without directly managing the complexity of all provider relationships simultaneously.
2Loss of energy
If traffic distribution is optimized based on multiple metrics including performance, capacity, cost, and availability, then service cost is reduced, but the time and effort required to distribute user traffic increases
Solution Approach 1:
The patent implements a simulation system that performs preliminary modeling and analysis of traffic distribution scenarios before actual traffic routing decisions are made. By pre-simulating various distribution strategies and their expected outcomes, the system prepares optimal routing plans in advance, reducing real-time decision-making time while optimizing for cost, performance, capacity, and availability metrics.
Solution Approach 2:
The patent establishes a feedback loop where performance metrics from CDN providers are continuously collected, analyzed through simulation, and used to adjust traffic distribution decisions. This feedback mechanism enables the system to learn from actual performance data and refine its routing strategies over time, reducing the manual effort required for optimization while maintaining cost efficiency.
3Adaptability or versatility
If multiple CDN providers are managed as a collective resource pool with dynamic routing, then traffic distribution flexibility is improved, but the difficulty of detecting and measuring performance accurately increases
Solution Approach 1:
The patent creates a universal metrics collection system that can gather, normalize, and analyze performance data from multiple different CDN providers regardless of their specific service characteristics. This multi-functional metrics system handles diverse provider offerings (different technologies, pricing models, performance characteristics) through a unified measurement framework, enabling accurate performance detection while maintaining traffic distribution flexibility.
Solution Approach 2:
The patent replaces manual performance measurement and analysis with an automated simulation system that computationally models performance outcomes. Instead of relying on complex manual monitoring and analysis of multiple providers, the system uses simulation algorithms to predict and measure performance metrics, simplifying the detection and measurement process while preserving the flexibility to manage diverse providers as a collective resource pool.
Data Source
AI summary
Techniques are disclosed for distributing data in a content delivery network configured to provide edge services using a plurality of service providers. Data indicative of data usage and cost data for the plurality of service providers is accessed. Based on the accessed data, an effective unit cost, multiplex efficiency, and channel utilization are determined for a selected user. A Bayesian optimization algorithm is applied to at least a portion of the accessed data. The content delivery network is configured to redistribute data traffic for the selected user based on a result of the applied Bayesian optimization algorithm.