Geographically Distributed Cache Memory for Digital Media Delivery Cost Reduction
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Solution Overview
Problem
Existing technologies face challenges in efficiently delivering multimedia content by optimizing asset engagement cycles, including presentation, selection, delivery, and consumption, while minimizing costs and maximizing value gain.
Innovation Solution
The proposed solution involves a system and method for pre-computation and geographically-distributed storage of digital assets based on predictions of future geo-specific consumption. This includes the use of an Asset Portfolio Manager (APM) to determine the optimal storage locations, asset sets, and delivery strategies to maximize value gain while reducing computation, storage, and delivery burdens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If digital media assets are delivered from centralized servers to users globally, then delivery coverage and accessibility are improved, but delivery costs and network latency increase
Solution Approach 1:
The patent segments the centralized asset delivery system into multiple geographically distributed cache servers. Each cache server stores copies of popular assets locally, serving users in its regional area. This segmentation reduces the distance assets must travel across the network, lowering delivery costs and latency while maintaining global coverage.
Solution Approach 2:
The patent implements preliminary action by pre-populating geographically distributed cache servers with digital media assets before users request them. The system predicts which assets will be popular in which regions and proactively caches them in advance, so when users request assets, delivery occurs from the nearest cache rather than from a distant centralized server, reducing delivery costs and improving speed.
2Productivity
If more cache memory is distributed geographically to reduce delivery costs, then delivery efficiency is improved, but storage infrastructure complexity increases
Solution Approach 1:
The patent makes the cache memory system universal by enabling cache servers to dynamically serve multiple different assets and multiple different users simultaneously. The same cache infrastructure can store and deliver various types of digital media assets (videos, images, documents) to different user groups based on predicted demand, reducing the need for dedicated storage systems for each asset type or user group.
Solution Approach 2:
The patent implements dynamics by making the cache content adaptive and changeable over time. Cache servers dynamically adjust which assets they store based on real-time and historical usage patterns, user behavior analysis, and predicted future demand. This dynamic reconfiguration allows the system to optimize delivery efficiency for different time periods and user groups without requiring permanent dedicated storage for each asset.
3Loss of energy
If assets are pre-computed and stored in geographically distributed locations based on predicted demand, then delivery costs are reduced, but computation and data management burden increases
Solution Approach 1:
The patent implements self-service by enabling the cache memory system to automatically manage its own content without requiring extensive manual intervention. The system uses algorithms to autonomously analyze user behavior patterns, predict which assets will be popular in which regions, and automatically transfer and cache the appropriate assets in advance. This self-service capability reduces the manual data management burden while achieving cost-effective delivery.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors actual asset usage against predicted usage patterns. This feedback information is used to refine and improve the prediction algorithms over time, making the pre-computation and caching strategy more accurate. The feedback loop allows the system to learn from past performance and automatically adjust its data management decisions, reducing the ongoing management burden.
Data Source
AI summary
An anchor asset is selected among a plurality of assets based on a caching gain calculation for the anchor asset. A variety of assets are selected from the plurality of assets based on a relationship with the anchor asset, the variety of assets including any combination of: audio, video, text, image, etc. The anchor asset and the variety of assets are cached on one or more geographically diverse servers among a plurality of servers for delivery to client devices.