Location-Based Edge Storage Nodes for Low-Latency User Data Access
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Solution Overview
Problem
Mobile computing devices interacting with cloud computing services experience limited data retrieval speeds and latency due to network traffic and lack of location-specific data, leading to wait times and potential privacy issues with data sharing.
Innovation Solution
Implementing edge computing storage nodes that are location-specific, using machine learning models to predict user interests and activities, and transferring relevant data from cloud to edge storage for faster access and enhanced privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored in cloud computing environments, then data availability and processing capabilities are improved, but data retrieval speed and latency increase due to network traffic distance
Solution Approach 1:
The system segments data storage into two parts: cloud computing environment for general data availability and edge computing node for location-specific fast access. This segmentation allows each component to serve its specific function optimally without compromising the other.
Solution Approach 2:
The edge computing node acts as an intermediary between the mobile device and the cloud computing environment. It caches location-specific data locally, serving as a mediator that reduces network traffic distance and improves retrieval speed while maintaining cloud connectivity for comprehensive data availability.
2Adaptability or versatility
If cloud computing environments provide comprehensive data services, then data processing capabilities are improved, but location-specific relevance and privacy control deteriorate
Solution Approach 1:
The system implements local quality by storing location-specific data at the edge computing node closer to the user's physical location. This ensures that the data processed is both comprehensive (from cloud) and locally relevant (from edge node), improving location-specific relevance while reducing unnecessary data transmission distance.
Solution Approach 2:
The system extracts location-specific data from the broader cloud computing environment and stores it at the edge computing node. This extraction separates the essential location-specific information from the general cloud data, enabling privacy control by limiting what is transmitted and processed, while maintaining comprehensive processing capabilities through cloud access.
Data Source
AI summary
There are provided systems and methods for edge computing storage nodes based on location and activities for user data separate from cloud computing environments. A service provider, such as an online transaction processor, may provide additional services for to users via edge computing systems and edge computing storage nodes. The service may be for data that may be predictively loaded to the edge computing storage node for a particular location, where the edge computing storage node may reside more locally to the location on a network so that data may be served quicker and with less network resource consumption than providing data from a remote cloud computing storage. The data may be predicted to be needed or useful to the user at the location using a user profile for the user, monitored user activities, and/or one or more machine learning models that predict user behaviors at the location.