Edge Cloud Prediction for Mobile User Latency
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Solution Overview
Problem
In mobile edge computing, finding the next closest and best edge cloud for a mobile user device while minimizing latency is challenging due to the dynamic movement of devices and limited power resources, leading to high energy consumption and inefficient data transfer.
Innovation Solution
A method and system that involves a mobile user device sending localization information to a base station, which uses machine learning to predict the device's route and determine the next nearest and best base station for offloading computational tasks, allowing for efficient data transfer and processing, even when the device moves out of the initial network cell.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If the mobile device transfers computational data to the nearest base station without prediction, then the data transfer is simple and direct, but the latency increases when the device moves to a different network cell
Solution Approach 1:
The system performs preliminary actions by predicting the next base station before the mobile device actually moves to it. The MEC cloud determines an expected next base station based on localization information and sends processing data in advance to this predicted base station, so that when the device arrives, the data is already there, significantly reducing latency
Solution Approach 2:
The MEC cloud acts as an intermediary that coordinates between the mobile device, current base station, and predicted next base station. It receives localization information, determines the expected next base station, and manages the pre-transfer of processing data, serving as a central intelligence that optimizes the data transfer path
2Reliability
If the mobile device continuously monitors and switches to the nearest base station, then the connection remains optimal, but the energy consumption increases
Solution Approach 1:
The system uses preliminary action by predicting the next base station in advance based on localization information and movement patterns. This allows the device to switch connections proactively rather than reactively, maintaining optimal connection quality while reducing the frequency and cost of continuous monitoring
Solution Approach 2:
The system implements feedback by using localization information from the mobile device to continuously update the prediction of the next base station. This feedback loop allows the MEC cloud to adapt to the device's movement and maintain accurate predictions, ensuring connection quality without requiring constant device-side monitoring
3Productivity
If the system waits for the mobile device to move before transferring data, then the data transfer is simpler, but the latency increases significantly
Solution Approach 1:
The system performs preliminary data transfer to the expected next base station before the mobile device actually moves to that cell. The MEC cloud determines the predicted next base station using localization information and sends the processing data in advance, ensuring that when the device arrives, processing can continue without interruption and with minimal delay
Solution Approach 2:
The system changes the parameter of data transfer timing from reactive (after movement) to proactive (before movement). By using localization information to predict future position, the system transforms the transfer trigger from a position-based event to a prediction-based event, achieving both continuity and speed
Data Source
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AI summary
The invention provides a method for finding a next edge cloud for a mobile user device in a mobile communication network for cloud processing, wherein the network comprises at least two base stations with computing access point functionality and the base stations being part of a mobile edge computing, MEC, cloud, wherein the method comprises the steps of: the mobile user device sending a request for usage privilege for cloud processing to a connecting base station, the connecting base station accepting the cloud processing request, the mobile user device offloading the computation processing data to the mobile edge computing cloud via the connecting base station and the mobile user device also sending localization information to the base station, the mobile edge computing cloud processing the computation processing data and determining an expected next base station based on the received localization information, the mobile edge computing cloud delivering the processed computation processing data to the determined expected next base station, when the user mobile device has moved from the network cell serviced by the connecting base station to the network cell of the determined expected next base station or to the connecting base station, when the mobile user device has not moved out of the network cell serviced by the connecting base station.