Cost-Aware Cloud Content Delivery Caching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The increasing demand for video content delivery in communication networks is hindered by the need for multiple versions of videos to accommodate diverse devices, leading to computationally expensive transcoding and significant storage requirements.
Innovation Solution
A cost-aware content delivery system determines a fraction of content item versions to cache based on popularity distribution and cost model information, including storage, transcoding, and transfer costs, using a processor and memory to optimize caching and transcoding decisions in centralized or distributed data centers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple versions of videos are stored to accommodate diverse devices, then adaptability is improved, but storage capacity requirements increase significantly
Solution Approach 1:
The system performs preliminary action by pre-transcoding videos into multiple versions before they are requested. A fraction of the most popular video versions are cached in advance, allowing the system to meet diverse device requirements without storing all possible versions, thus reducing storage capacity needs while maintaining adaptability.
Solution Approach 2:
The system changes the parameter of version selection by using popularity distribution to determine which video versions to cache. Instead of storing all versions, the system stores only the top fraction of popular versions, dynamically adjusting the caching strategy based on demand patterns to balance adaptability and storage requirements.
2Adaptability or versatility
If video transcoding is performed to create multiple versions, then adaptability is improved, but computational cost increases significantly
Solution Approach 1:
The system performs preliminary transcoding action by pre-transcoding videos into multiple versions and caching them before requests arrive. By preparing the content in advance based on popularity predictions, the system avoids the need for real-time transcoding, significantly reducing computational cost while maintaining the ability to serve diverse devices.
Solution Approach 2:
The system applies partial action by only transcoding and caching a fraction of the most popular video versions rather than all possible versions. This selective approach reduces the total computational burden while still providing adequate adaptability for the majority of use cases, avoiding the excessive cost of preparing every possible version.
3Speed
If all video versions are cached to ensure fast delivery, then delivery speed is improved, but storage and operational costs increase
Solution Approach 1:
The system changes the caching parameter by storing only a fraction of video versions determined by popularity distribution. This allows the system to achieve fast delivery for the most popular content while avoiding the operational costs of storing and managing all possible versions, thus balancing delivery speed with operational efficiency.
Solution Approach 2:
The system applies partial caching by storing only the essential fraction of popular video versions rather than all versions. This partial action is sufficient to meet the majority of user demands with fast delivery, while avoiding the excessive storage and operational costs associated with caching every possible version.
Data Source
AI summary
A capability is provided for determining a fraction of content item versions to cache for use in responding to requests for content items. The fraction of content item versions to cache is determined based on a popularity distribution of the content item versions and cost model information associated with the content item versions. The cost model information may include information indicative of a cost of storing one of the content item versions and at least one of a cost of transcoding one of the content item versions or a cost of transferring one of the content item versions. The fraction of content item versions to cache may be determined based a skewness factor of the popularity distribution of the content item versions.