Joint allocation optimization method for computing resources and communication resources of vehicle-mounted edge network

Abstract

The invention discloses a joint allocation optimization method for computing resources and communication resources of a vehicle-mounted edge network, which comprises the following steps of: constructing an unloading framework based on mobile edge computing MEC, modeling the framework into a combined auction mode, and maximizing the benefit of an edge computing server provider under the condition that the computing resources and the communication resources are limited. Computing resources and communication resources in the Internet of Vehicles are subjected to combined auction, a seller is an MEC server with limited computing and communication resources, a buyer is a task vehicle purchasing the computing and communication resources, an application program with high energy consumption of a computing task is unloaded to the MEC server, and the MEC server runs a new complex program. The method comprises the following steps: firstly, classifying vehicle tasks by using a k-means algorithm, preferentially allocating resources for high-level task vehicles, secondly, constructing a preference list for networked vehicles, and finally, executing a resource allocation strategy. The benefit of the edge computing server provider is maximized on the premise of ensuring the task level.

Description

technical field [0001] The invention relates to the technical field of the Internet of Vehicles, in particular to a method for jointly allocating and optimizing computing resources and communication resources of a vehicle edge network. Background technique [0002] Networked vehicles form a vehicle ad hoc network through vehicle-to-infrastructure (V2I) links and vehicle-to-vehicle (V2V) links. Since the substantial growth of mobile applications brought by vehicle-mounted units has brought new challenges to this technology, offloading computing tasks to the cloud is considered to be a promising approach, but the long-distance deployment brings the backbone network and The capacity limitation and delay fluctuation of the backhaul network lead to a serious decline in vehicle QoS (Quality of Service). Mobile edge computing (Mobile Edge Computing, MEC) provides computing and communication services at the edge of the mobile network, which not only meets the expansion needs of veh...

AI Technical Summary

Problems solved by technology

Taking cloud computing as an example, a remote cloud server with a large amount of computing and storage capacity is responsible for providing computing and storage services to users. However, the network is only used for data transmission between the cloud and users, so it cannot meet the delay and service quality of networked vehicles. Require

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