Dynamic CDN Entity Routing for Adaptive Traffic Distribution
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Solution Overview
Problem
Content delivery network entities face inefficiencies due to the use of static selection parameters, which fail to optimally select the most suitable network entities as conditions change rapidly, leading to suboptimal bandwidth utilization and increased rebuffering during peak demand.
Innovation Solution
A dynamic adjustment mechanism that uses real-time usage data to adjust selection parameters for content delivery network entities, ensuring they handle more or less traffic based on recent data traffic levels, thereby optimizing their usage and improving network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static selection parameters are used to select content delivery network entities, then the system is simple to operate, but the system cannot optimally select network entities when conditions change rapidly
Solution Approach 1:
The system automatically monitors data traffic levels and adjusts selection parameters without manual intervention. The service performs self-adjustment by detecting when a content delivery network entity exceeds its data traffic limit and automatically modifying the selection process to distribute traffic appropriately, eliminating the need for external configuration changes.
Solution Approach 2:
The system continuously monitors data traffic levels for each content delivery network entity and uses this feedback to dynamically adjust selection parameters. When traffic levels indicate that an entity is experiencing high demand, the system receives feedback about this condition and automatically modifies routing decisions to balance the load across available entities.
2Productivity
If static parameters are used for content delivery network selection, then configuration is simple, but bandwidth utilization is suboptimal during peak demand
Solution Approach 1:
The system transitions from static selection parameters to dynamic parameters that automatically adjust based on real-time data traffic levels. The selection process becomes dynamic, adapting to changing network conditions by monitoring traffic patterns and modifying routing decisions to optimize bandwidth utilization and reduce rebuffering events during peak demand periods.
Solution Approach 2:
The system changes the selection parameters based on monitored data traffic levels. When traffic levels for a content delivery network entity exceed predetermined thresholds, the system modifies the parameters used to select entities, thereby redistributing traffic to optimize bandwidth utilization and reduce rebuffering time during high-demand periods.
3Speed
If manual parameter changes are required to adapt to changing conditions, then system complexity is reduced, but response time to changing network conditions increases
Solution Approach 1:
The service automatically monitors data traffic levels and adjusts selection parameters without requiring manual intervention. The system performs self-adjustment by detecting when content delivery network entities exceed their traffic limits and automatically modifying routing decisions, achieving fast response to changing conditions while maintaining operational simplicity.
Data Source
AI summary
In some embodiments, a method receives usage data that is based on a delivery of content by a plurality of content delivery network entities. A first value of a selection parameter is used to determine whether to select a content delivery network entity from the plurality of content delivery network entities to process a first request for content. The method allocates the usage data in a first distribution to the plurality of content delivery network entities. The allocating does not use a condition to determine the first distribution. The usage data is allocated in a second distribution to the plurality of content delivery network entities. The allocating uses the condition to determine the second distribution. The method adjusts the first value of the selection parameter to a second value based on the first distribution and the second distribution.