Predictive Application Context Relocation for Edge Service Continuity
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Solution Overview
Problem
Existing wireless communication systems face challenges in maintaining edge service continuity and optimizing application context relocation in mobile edge cloud deployments, particularly due to UE mobility, network overload, and performance variations across edge data networks.
Innovation Solution
The implementation of predictive application context relocation procedures, which involve receiving predicted UE routes and data analytics parameters, determining predictive trigger actions, and planning proactive application context relocations from one edge application server to another based on these inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If application context relocation is performed reactively (after UE moves), then network resource utilization is improved, but edge service continuity deteriorates due to service interruption during handover
Solution Approach 1:
The system performs preliminary action by predicting the UE's future location using analytics parameters and proactively relocating the application context to the target edge application server before the UE actually moves there. This advance relocation ensures service continuity while allowing the network to optimize resource allocation across multiple UEs based on predicted movements.
2Reliability
If application context is relocated frequently to follow UE mobility, then edge service continuity is improved, but network overhead and complexity increase
Solution Approach 1:
The system introduces an intermediary mechanism using analytics parameters and prediction algorithms that act as a mediator between UE mobility events and application context relocation decisions. This intermediary filters and processes mobility information, triggering relocation only when predicted UE location changes cross thresholds, thereby reducing unnecessary relocations and network overhead while maintaining service continuity.
3Measurement precision
If predictive analytics parameters are collected and processed, then application context relocation accuracy is improved, but network processing load increases
Solution Approach 1:
The system applies partial action by selectively processing analytics parameters based on prediction confidence levels and predefined thresholds. Instead of continuously processing all available analytics data, the system processes parameters only when prediction accuracy metrics indicate a high-confidence relocation opportunity, thereby maintaining relocation accuracy while reducing unnecessary network processing load.
Data Source
AI summary
Various aspects of the present disclosure relate to predictive application context relocation. One apparatus is configured to receive a predicted route for a user equipment (“UE”), receive one or more data analytics parameters for at least one of a radio access network, a core network, or an edge data network associated with the predicted route of the UE, determine at least one predictive trigger action for remapping an application client from a first edge application server of a first edge data network to a second edge application server of a second edge data network based on the predicted route for the UE and the one or more data analytics parameters, and determine an application context relocation for the application client based on the at least one trigger action.