Social Graph Precaching for Edge Storage Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current precaching systems face challenges in efficiently managing social media content distribution due to high costs associated with storing large media files and difficulties in determining optimal caching locations, leading to unnecessary bandwidth consumption and storage inefficiencies.
Innovation Solution
A method that analyzes a user's social graph connections to predict which contacts are likely to access specific media objects, allowing for intelligent precaching of these objects at geographically optimal locations based on access probabilities and available storage capacity, thereby reducing latency and storage needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If precaching is performed for all social media content, then content accessibility is improved, but storage costs and bandwidth consumption increase significantly
Solution Approach 1:
The patent implements location-aware precaching by determining geographic locations of user devices and selectively caching content at edge servers in those specific locations. This ensures content is stored locally where it is actually needed rather than universally, reducing overall storage requirements while maintaining accessibility for users in relevant geographic areas.
Solution Approach 2:
The system performs preliminary precaching of content to edge servers based on predicted user access patterns before users actually request the content. By using social graph analysis and location data to predict which users are likely to access which content, the system prepares content in advance at appropriate locations, improving accessibility without caching all content everywhere.
2Reliability
If precaching is performed without location awareness, then content accessibility is improved, but bandwidth consumption increases due to unnecessary caching
Solution Approach 1:
The patent determines the geographic location of user devices and selectively precaches content at edge servers in those specific locations. This location-aware approach ensures content is cached only where users are actually present and likely to access it, eliminating wasteful bandwidth consumption from caching content in locations where no users are present.
Solution Approach 2:
The system performs preliminary precaching actions by predicting which users are likely to access which content based on social graph relationships and current location data. Content is precached in advance at edge servers in the predicted user locations, improving accessibility while minimizing bandwidth waste by avoiding caching in locations where users are not present.
3Productivity
If social graph analysis is used for precaching, then caching efficiency is improved, but system complexity increases
Solution Approach 1:
The patent performs preliminary analysis of social graph relationships to identify which users are connected and likely to share or access the same content. This pre-computed social graph information is then used to guide precaching decisions, improving caching efficiency by focusing resources on content likely to be accessed by connected users without requiring complex real-time analysis during content delivery.
Solution Approach 2:
The system introduces an intermediary precaching layer between content sources and users, managed by edge servers that use social graph analysis to make intelligent caching decisions. This intermediary layer handles the complexity of social graph processing and location-aware content selection, shielding the rest of the system from complexity while improving overall caching efficiency.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A precaching system identifies an object, such as a media file, that a user accesses and then analyzes a social graph of the user to identify social graph contacts that may be interested in the object. Based on the content of the object -- and the interests and connections of contacts in the social graph -- the precaching system determines whether a particular contact in the user's social graph is likely also to access the object. For example, the precaching system may determine a hit score corresponding to the object and a likelihood that the particular contact in the social graph will access the object. If the precaching system determines that the likelihood that the particular contact will access the object meets or exceeds a threshold probability level for precaching the object, the precaching system precaches the object near the contact in anticipation that the contact will access the object.