Vehicle Internet Service Handover Using Predictive MEC VM Replication

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Solution Overview

Problem

The challenge in vehicular networks is optimizing the selection of Mobile Edge Computing (MEC) hosts to ensure reliable and seamless service continuity under mobility constraints, minimizing energy consumption and latency, especially in urban environments with densely deployed 5G networks.

Innovation Solution

A method for dynamically selecting MEC hosts based on mobility prediction and uncertainty, using a Lyapunov-based optimization framework to minimize energy consumption and service discontinuity risk by proactively replicating virtual machines (VMs) to multiple potential MEC hosts before handovers, balancing migration costs and reliability.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If virtual machines are proactively replicated to multiple potential MEC hosts before handovers, then service continuity and reliability are improved, but energy consumption increases

Engineering Contradiction:

Improveservice continuityVSAvoidenergy consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary actions by proactively replicating virtual machines to potential target MEC hosts before handover events occur. This advance preparation ensures service continuity is maintained while allowing the system to optimize energy consumption by selecting appropriate replication strategies based on predicted mobility patterns and service requirements

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically adjusts replication parameters including the number of replicas, selection of target hosts, and replication timing based on changing conditions such as vehicle mobility predictions, network state, and service quality requirements. This allows optimization of the trade-off between service continuity reliability and energy consumption

Inventive Principle:

Principle #35Parameter changes

2Reliability

If virtual machines are migrated to follow the vehicle, then service continuity is maintained, but migration complexity and energy consumption increase

Engineering Contradiction:

Improveservice continuityVSAvoidmigration complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system performs preliminary migration actions by proactively transferring virtual machines to predicted target MEC hosts before handover events. This approach simplifies the actual migration process during handover while maintaining service continuity, as the migration is prepared in advance based on mobility predictions rather than reacting to handover events

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates copies of virtual machines at target MEC hosts before or during handover events. These replicas enable seamless service continuation without requiring complex real-time migration operations, as the vehicle can switch to a pre-prepared replica rather than undergoing complex migration procedures

Inventive Principle:

Principle #26Copying

3Reliability

If more virtual machine replications are performed, then service continuity risk is reduced, but total energy consumption increases

Engineering Contradiction:

Improveservice continuityVSAvoidenergy expenditure

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The system dynamically changes replication parameters including the number of replicas, selection of target hosts, and replication timing based on changing conditions such as vehicle mobility predictions, network state, and service quality requirements. This allows optimization of the trade-off between service continuity reliability and energy consumption

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system applies partial replication strategies where only necessary virtual machines are replicated to necessary extent based on service requirements and predicted mobility patterns. This avoids excessive replication that would waste energy while ensuring sufficient replication to maintain service continuity for critical services

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12563366B2Method of managing a vehicle internet service in a cellular network of at least fourth generation 4G

Publication Date: 2026.02.24 RENAULT SA

AI summary

A method of managing a vehicle Internet service in a cellular network is provided. For each time interval: a vehicle, equipped with a control unit, sends a request to perform exchange of data linked to a remote service on a current server associated with the current geographic area of coverage. The current server includes a virtual machine to perform operations of the remote service. When the vehicle is on outskirts of current geographical area a distance from the area less than a first threshold, the current server estimates the value of a probability vector. The current server estimates value of risk per request and value of energy per request necessary for parallel replications of the virtual machine of the request on the servers corresponding to zones of geographical coverage contiguous to the current zone; and and estimates a number of replications of the virtual machine linked to the request to be made.