Smart Delivery Node Neural Network Traffic Categorization
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Solution Overview
Problem
Content delivery networks (CDNs) face performance degradation when handling a mixture of different types of traffic, as they are typically optimized for video traffic, leading to inefficiencies and increased bandwidth usage on origin servers.
Innovation Solution
Implementing a method within delivery nodes that uses a trained neural network to categorize content and redirect requests to the most suitable delivery nodes based on traffic patterns, optimizing access policies and improving resource allocation across the CDN.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If delivery nodes are optimized for video traffic, then video delivery performance is improved, but performance degrades when handling mixed traffic types
Solution Approach 1:
The patent segments traffic into different categories (video traffic and non-video traffic) and routes them to different delivery nodes. Delivery nodes are specialized to handle specific traffic types, with video-optimized nodes for video content and general-purpose nodes for other content types. This segmentation resolves the contradiction by allowing each node type to be optimized for its specific function while the system as a whole handles diverse traffic types.
2Productivity
If delivery nodes cache and deliver small data files for web traffic, then web traffic handling is improved, but storage I/O performance deteriorates
Solution Approach 1:
The patent applies local quality by creating delivery nodes with different characteristics suited to different traffic types. General-purpose delivery nodes are configured with storage and I/O optimizations appropriate for web traffic patterns, while video-optimized nodes have different configurations. This allows each node type to have local optimizations that reduce I/O overhead for its specific traffic type while maintaining overall system versatility.
3Adaptability or versatility
If delivery nodes handle both video and web traffic, then CDN versatility is improved, but overall performance degrades
Solution Approach 1:
The patent introduces a request router as an intermediary component that receives client requests, categorizes the traffic type, and routes requests to appropriate specialized delivery nodes. This intermediary layer enables the CDN to handle diverse traffic types effectively by directing each request to the most suitable node, thereby maintaining high overall performance while preserving system versatility.
Data Source
AI summary
A method, Delivery Node, DN, and Content Delivery Network, CDN, are optimized to deliver content of different categories. The DN is operative to receive a request for a content, obtain a determination of whether or not a category of content is associated with the requested content and responsive to the determination that no category of content is associated with the requested content, forward the request for the content towards an origin server and upon receiving a response from the origin server serve the content. The CDN comprises a plurality of DNs, a data processing service operative to obtain, from the DNs, and assemble, data sets into training and validation data formatted to be used for training and validating a Neural Network, NN. The CDN comprises a NN training service, operative to train and validate the NN and a configuration service, operative to configure the plurality of DNs with the trained and validated NN.