Intelligent SDN Controller for Custom CDN Provisioning
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Solution Overview
Problem
Conventional static software-defined networks (SDNs) are limited in managing content delivery networks (CDNs) as they only consider Layer 2 and Layer 3 parameters, ignoring additional information such as user profiles, equipment status, and environmental conditions, which restricts their ability to optimize network resource management.
Innovation Solution
An intelligent software-defined network (ISDN) that leverages user/device/customer profile information, equipment information, and environmental conditions to dynamically determine and update a custom content delivery network (CCDN), enabling adaptive provisioning of network segments based on real-time or near real-time changes in demand and user behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional static SDN components are used for CDN selection, then the system structure is simple and easy to implement, but the network resource management efficiency is insufficient due to limited use of additional information
Solution Approach 1:
The patent transforms the static SDN controller into a dynamic intelligent SDN controller that can adaptively adjust CDN selection based on real-time network conditions, user profiles, and equipment states. The system dynamically determines optimal CDN configurations by processing multiple information sources and updating routing decisions continuously, resolving the contradiction between system simplicity and management efficiency.
Solution Approach 2:
The patent introduces an intelligent SDN controller as an intermediary layer between the network infrastructure and CDN selection process. This intermediary component aggregates and analyzes diverse information sources (user profiles, equipment states, network conditions) and translates them into optimized routing decisions, enabling efficient resource management without requiring fundamental changes to the underlying network architecture.
2Adaptability or versatility
If only Layer 2 and Layer 3 parameters are used for CDN designation, then the configuration is simple, but the adaptability to different network conditions and user requirements is limited
Solution Approach 1:
The patent segments the information processing task into distinct modules: user profile analysis, equipment state monitoring, network condition assessment, and CDN selection optimization. Each module processes specific types of information independently, then integrates results to make comprehensive routing decisions. This segmentation enables the system to handle diverse information sources without overwhelming complexity.
Solution Approach 2:
The patent extends the traditional Layer 2 and Layer 3 parameter framework by incorporating additional parameters from user profiles, equipment states, and network conditions. The intelligent SDN controller processes these expanded parameters to make more informed CDN selection decisions, thereby increasing adaptability while managing complexity through structured parameter handling.
3Productivity
If static rules are used for CDN selection, then the decision-making process is simple and fast, but the ability to optimize network resources based on real-time demands is insufficient
Solution Approach 1:
The patent implements preliminary action by pre-processing and storing user profiles, equipment states, and network condition data before actual CDN selection is needed. The intelligent SDN controller maintains updated information repositories and pre-computes optimization parameters, so that when content delivery requests occur, the system can quickly retrieve and apply pre-analyzed data rather than performing full analysis in real-time.
Solution Approach 2:
The patent establishes feedback mechanisms where the intelligent SDN controller continuously monitors CDN performance, user satisfaction metrics, and network resource utilization. This feedback information is fed back into the decision-making process to continuously optimize CDN selection, enabling the system to improve content delivery efficiency over time while maintaining relatively fast response times through learned optimization patterns.
Data Source
AI summary
Determining a custom content delivery network is disclosed. This can comprise determining a custom content delivery network (CCDN) based on information determined by an intelligent software-defined network (ISDN). An ISDN can receive a content request and related information from a user equipment (UE). The ISDN can determine CCDN information that can be employed to provision a transport network corresponding to the CCDN information. The transport network can be customized based on the content request and the related information. Moreover, as the related information changes, the transport network can be correspondingly updated. Some embodiments disclose an ISDN operating on a virtual machine in a cloud-computing environment.