Edge Node Pre-Reading Based on Data Relevance
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Solution Overview
Problem
In Edge/Core cooperation storage systems, existing data pre-reading techniques are inefficient due to limited Edge node capacity, leading to unnecessary data deletion and increased data transmission between nodes, which hampers response times and bandwidth usage.
Innovation Solution
An information processing device with a relevance calculation module determines which data to pre-read and delete based on access history and storage capacity, prioritizing data relevance and minimizing unnecessary data transmission by only deleting data when storage is insufficient.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is pre-read into the Edge node storage device, then client data retrieval speed is improved, but storage capacity is consumed and may require deletion of other data
Solution Approach 1:
The system pre-reads data into the Edge node storage device before client requests occur, based on prediction of future access patterns. This preliminary action stores data in advance (e.g., pre-loading movie data before a client requests it), improving retrieval speed while managing storage capacity through selective pre-reading based on relevance calculations.
2Quantity of substance
If data is deleted from the Edge node to free storage capacity, then storage availability is improved, but data retrieval requires re-reading from the Core node increasing transmission time
Solution Approach 1:
The system changes the parameter of data selection from simple FIFO (first-in-first-out) or random deletion to relevance-based deletion. By calculating relevance scores based on access history and prediction results, the system identifies which data to retain and which to delete, optimizing the balance between storage availability and minimizing re-reading time from the Core node.
3Speed
If all files are pre-read into the Edge node, then immediate response to client requests is achieved, but the limited Edge node capacity cannot accommodate all files
Solution Approach 1:
The system extracts only the necessary subset of data for pre-reading based on relevance calculations, rather than attempting to pre-read all files. By identifying and extracting high-relevance data (e.g., data likely to be accessed soon based on access patterns), the system achieves fast response times for predicted requests while staying within the limited Edge node storage capacity.
4Device complexity
If data deletion is based on predetermined time without access, then storage management is simplified, but useful data may be deleted that clients will request in the future
Solution Approach 1:
The system implements feedback-based data management by continuously monitoring access history and using it to calculate relevance scores. This feedback loop allows the system to adapt deletion decisions based on actual access patterns rather than fixed time rules, improving data availability reliability while maintaining manageable complexity through automated relevance-based decision making.
Data Source
AI summary
Efficient pre-reading is performed in data transmission and reception between an Edge node and a Core node. An information processing device includes a storage device, outputs client request data based on a request of a client, and stores predetermined pre-read data in the storage device before the request of the client. The device includes: a relevance calculation module configured to calculate relevance between data based on an access history of the data; and a pre-reading and deletion module configured to determine data to be deleted from the storage device using the relevance when data having predetermined relevance with the client request data is to be stored to the storage device as the pre-read data and a storage capacity of the storage device is insufficient if at least one of the client request data and the pre-read data is to be stored to the storage device.