Edge Computing Migration via Trajectory Prediction
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Solution Overview
Problem
Current edge computing systems face challenges in seamlessly migrating computing resources between cells in a cellular network, leading to latency and downtime during handovers, due to imprecise location data and dependence on UE communication.
Innovation Solution
A system that predicts handover events by determining the trajectory of user equipment using beam/signal strength and Massive MIMO antenna positioning information, allowing for proactive resource allocation and migration of computing information between edge computing devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If trajectory prediction and proactive resource allocation are implemented, then migration latency is reduced, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by determining UE trajectory and predicting handover events before they occur. The network device calculates future positions based on current trajectory data and proactively allocates resources at target edge computing devices, enabling seamless migration without waiting for actual handover to initiate.
Solution Approach 2:
The network device acts as an intermediary between the UE and edge computing devices. It receives trajectory information, performs prediction calculations, coordinates resource allocation across multiple edge devices, and manages the migration process, thereby reducing the complexity burden from individual devices while improving overall system efficiency.
2Reliability
If proactive resource allocation is performed, then service continuity is improved, but network signaling overhead increases
Solution Approach 1:
Resource allocation is performed in advance based on predicted handover events. The network device sends resource allocation messages to target edge computing devices before the UE actually hands over, ensuring resources are ready and service continuity is maintained without requiring extensive real-time signaling during the critical handover moment.
Solution Approach 2:
The system uses existing trajectory information that is already being collected for other purposes (such as positioning and mobility management). This pre-existing data is repurposed for handover prediction, reducing the need for additional dedicated signaling channels while maintaining service continuity.
3Measurement precision
If three-dimensional location tracking is implemented, then positioning accuracy is improved, but energy consumption increases
Solution Approach 1:
The network device serves as an intermediary that performs the computationally intensive three-dimensional trajectory calculation and prediction tasks. The UE provides raw positioning data with minimal processing, while the network device handles the complex calculations, thereby achieving high positioning accuracy without burdening the UE's energy resources.
Solution Approach 2:
The system replaces local computational mechanics at the UE with centralized computational resources at the network device. Instead of the UE performing complex trajectory analysis locally (which would consume significant energy), the calculation is performed remotely at the network device using the same data.
4Ease of operation
If seamless migration is achieved, then user experience is improved, but network control complexity increases
Solution Approach 1:
The network device acts as a central coordinator that manages the complexity of seamless migration. It receives trajectory information, predicts handover events, coordinates resource allocation across multiple edge computing devices, and ensures smooth transitions. This centralized mediation provides seamless user experience while consolidating control complexity in one entity rather than distributing it across multiple devices.
Solution Approach 2:
By performing preliminary resource allocation and handover preparation before actual handover events, the system ensures seamless migration without requiring complex real-time coordination during the critical transition moment. The preliminary actions pre-resolve potential conflicts and simplify the actual handover execution.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Reduces latency and ensures seamless migration by preparing resources and data in advance, supporting low-end devices without relying on UE sensors or additional communication, and providing accurate three-dimensional location tracking.
Implementation Method 1
determining the trajectory of user equipment using beam/signal strength and Massive MIMO antenna positioning information
Implementation Method 2
beam/signal strength and Massive MIMO antenna positioning information
Data Source
AI summary
A method for enabling migration of computing information between a first edge computing device associated with a first cell in a cellular network and at least a second edge computing device associated with a second cell in the cellular network, wherein the cellular network comprises a plurality of cells, the method comprising determining the trajectory of a wireless communication device located within a first cell of the cellular network, identifying a second cell of the cellular network towards which the trajectory leads, causing allocation of resources at the second edge computing device associated with the second cell to enable migration of computing information from the first edge computing device associated with the first cell to the second edge computing device associated with the second cell.