IoV Service Caching and Task Migration for Low-Latency Edge Computing
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Solution Overview
Problem
In the Internet of Vehicles (IoV), the lack of computational resources and energy supply in vehicles, combined with the long distances to cloud servers, leads to high latency and network congestion, especially for latency-sensitive tasks, while relying solely on cloud computing exacerbates backhaul load and fluctuating edge resource availability.
Innovation Solution
A method for service caching and task migration using a multi-intelligence body reinforcement learning approach, involving a system model with device, edge, and cloud layers, optimizing service caching and task migration through a Markov decision process to balance latency, energy consumption, and migration costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If cloud computing is used to process computational tasks, then computational resources are improved, but transmission latency increases due to long distance between cloud servers and vehicles
Solution Approach 1:
The patent segments the centralized cloud computing system into a hierarchical structure with cloud servers, edge servers (MEC servers at base stations), and vehicle users. This segmentation brings computational resources closer to vehicles through edge servers, reducing transmission latency while maintaining access to cloud-level computational capabilities.
Solution Approach 2:
The patent introduces edge servers as intermediary components between cloud servers and vehicles. These edge servers cache services and process tasks locally, acting as mediators that reduce the distance data must travel while still leveraging cloud resources when needed, thus resolving the latency-resource tradeoff.
2Loss of time
If mobile edge computing is used to reduce latency, then transmission latency is improved, but edge resource availability fluctuates due to dynamic network topology
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors edge server resource status, vehicle locations, and service request patterns. This feedback enables dynamic adjustment of service caching strategies and task migration decisions, maintaining reliable service delivery despite fluctuating edge resource availability.
Solution Approach 2:
The patent employs dynamic service caching and task migration strategies that adapt to changing network conditions, vehicle mobility, and edge resource availability. The system can dynamically decide whether to cache services at edge servers, migrate tasks between edge servers, or redirect to cloud servers, ensuring continuous reliable service delivery.
3Loss of time
If services are cached at edge servers to reduce latency, then transmission latency is improved, but system complexity increases due to joint optimization of caching and migration
Solution Approach 1:
The patent segments the complex joint optimization problem into separate but coordinated sub-problems: service caching decisions at edge servers and task migration decisions between edge servers. This segmentation allows each sub-problem to be addressed with specialized algorithms, reducing overall system complexity while achieving low-latency performance.
Data Source
AI summary
A method for service caching and task migration based on Internet of Vehicles (IoV) is provided. The method includes: establishing a system model based on the Internet of Vehicles, and constructing a service caching model and a task migration model based on the system model, constructing a joint optimization problem for service caching and task migration based on the service caching model and the task migration model, abstracting the joint optimization problem into a Markov decision process; and determining an optimal caching decision and an optimal migration decision by solving the joint optimization problem of service caching and task migration using a multi-intelligence body reinforcement learning method based on the Markov decision process. By jointly optimizing service caching and task migration, the present disclosure further reduces the latency and energy consumption in the service caching process and the cost of task migration, and improves the service quality of the system.