Edge Computing Resource Migration with Route-Based Timing
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Solution Overview
Problem
Existing edge computing systems face challenges in accurately predicting and timing the migration of computing resources for mobile devices due to inaccuracies in mobility prediction, leading to suboptimal provisioning and increased latency and bandwidth reduction.
Innovation Solution
An orchestration function in the mobile network predicts migration points based on a mobile device's planned or predicted route and collaborates with the device to initiate the migration of edge computing resources proactively, using heads-up notifications from the device to optimize the timing of the migration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If edge computing resources are migrated proactively based on predicted routes, then migration timing can be optimized and latency reduced, but prediction accuracy deteriorates leading to suboptimal provisioning
Solution Approach 1:
The system performs preliminary actions by predicting migration points in advance based on the mobile device's planned route and initiating the migration process before the device actually reaches the migration point. This allows the computing resource to be ready at the destination edge node, reducing migration latency and improving timing accuracy despite prediction imperfections.
2Device complexity
If migration is triggered reactively when device reaches edge node boundary, then prediction complexity is reduced, but latency increases and bandwidth is reduced
Solution Approach 1:
Instead of reacting when the device reaches the boundary, the system proactively identifies migration points in advance based on the planned route and initiates migration before the device arrives. This preliminary action reduces the time the device waits for migration completion, thereby reducing latency and improving bandwidth availability.
Solution Approach 2:
The system uses feedback from the mobile device's actual movement to refine the migration process. By receiving feedback about the device's real-time location and comparing it with the predicted route, the system can adjust migration timing dynamically, optimizing both latency and prediction accuracy.
3Extent of automation
If migration is initiated without device cooperation, then system automation increases, but timing precision deteriorates reducing network performance
Solution Approach 1:
The system implements feedback mechanisms where the mobile device provides information about its actual movement and location. This feedback is used by the orchestration function to refine migration timing decisions, ensuring that migrations are initiated at optimal moments that balance automation with timing precision for maximum network performance.
Data Source
AI summary
The invention relates to a system configured as an orchestration function for a mobile network, wherein the mobile network comprises edge nodes which are configurable to provide edge computing resources to mobile devices, wherein the system is configured to at least in part orchestrate a migration of an edge computing resource for a mobile device from a first edge node to a second edge node. The orchestration function and the mobile device interact to enable the orchestration function to start the migration of the edge computing resource in a timely manner.


