MEC Controller Task Migration Latency Reduction
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Solution Overview
Problem
In multi-access edge computing (MEC) platform architectures, the migration of services between MEC nodes due to a mobile device's movement can incur significant latency and costs, negatively impacting user experience, as existing solutions fail to seamlessly switch execution between nodes while meeting quality of service (QoS) and latency requirements.
Innovation Solution
A method and apparatus where a MEC distributed controller receives geo-location information from a mobile device, determines an execution plan and a pool of MEC nodes based on this information, schedules the execution plan, and deploys it, allowing for efficient task partitioning and resource allocation to minimize task migration and ensure continuous service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If services are migrated between MEC nodes to follow mobile device movement, then service continuity is maintained, but task migration latency and costs increase
Solution Approach 1:
The system performs preliminary actions by predicting the mobile device's future location and proactively preparing task execution plans before migration is needed. The MEC distributed controller uses geo-location information to anticipate which MEC node will be optimal for serving the mobile device next, and pre-configures task assignments to minimize migration latency when the device moves.
2Power
If tasks are executed on remote cloud servers, then computational power is sufficient, but latency requirements cannot be met
Solution Approach 1:
The system applies local quality by executing tasks at the edge of the network (MEC nodes) closest to the mobile device rather than on remote cloud servers. This distributed execution model allows computationally intensive tasks to be performed locally at MEC nodes, providing both sufficient computational power and low latency by eliminating long-distance data transmission.
3Power
If mobile devices have high computational power, then they can execute complex tasks independently, but device size and power consumption increase
Solution Approach 1:
The system introduces an intermediary layer (MEC nodes) between the mobile device and the cloud. The mobile device offloads computationally intensive tasks to MEC nodes, which have the necessary computational power and energy resources. This allows the mobile device to access high computational power without carrying large batteries or high-power processors, as the MEC nodes serve as energy-rich intermediaries.
Data Source
AI summary
A method implemented by a multi-access edge computing (MEC) distributed controller includes receiving a MEC computing task request for execution of a service on MEC nodes controlled by the MEC distributed controller; obtaining mobile device geo-location information associated with the mobile device; determining an execution plan and a pool of MEC nodes, the execution plan and the pool of MEC nodes being for the execution of the service, the determining being in accordance with the mobile device geo-location information; scheduling the execution plan for the pool of MEC nodes; and deploying the scheduled execution plan.