Internet of vehicles cloud system resource distribution method based on multi-target genetic algorithm

Abstract

The invention provides an Internet of Vehicles cloud system resource distribution method based on a multi-target genetic algorithm, used for solving the technical problem that the blocking rate of anapplication request is relatively large in current Internet of Vehicles cloud system resource distribution. The method comprises the implementation steps of 1, building a multi-target optimization model; 2, setting the number of iterations and the maximum of iterations; 3, acquiring a t-th generation parent population Pt; 4, acquiring a t-th generation progeny population Qt; 5, combining the t-thgeneration parent population Pt with the t-th generation progeny population Qt; 6, acquiring (t+1)-th surrogate parent population P(t+1); and 7, acquiring a resource distribution result of the application request. According to the method provided by the invention, the minimum blocking rate and the minimum cost are used as target functions, the genetic algorithm is used for solving an optimal resource distribution result set, and under the requirement of meeting the cost, the block rate of the application request is reduced, and the resource utilization rate of the Internet of Vehicles cloud system is increased.

Description

technical field [0001] The invention belongs to the technical field of cloud computing, and relates to a resource allocation method for a cloud system of the Internet of Vehicles, in particular to a resource allocation method for the cloud system of the Internet of Vehicles based on a multi-objective genetic algorithm. Background technique [0002] The intelligent transportation system is one of the important development directions of the future transportation system. As a specific form of the Internet of Things technology applied to the field of intelligent transportation, the Internet of Vehicles is of great significance to the construction of my country's transportation system and national economic development. With the rapid development of the global automotive industry, many emerging in-vehicle applications have emerged, such as in-vehicle multimedia entertainment, in-vehicle social networking, and location-based services. These applications require complex computing cap...

AI Technical Summary

Problems solved by technology

This method considers the impact of vehicle resources and social relations on resource allocation results when establishing a continuous-time Markov model, and reduces the blocking rate of application requests to a certain extent. In the time Markov model, the impact of frequent interruptions of connections between vehicles caused by vehicle mobility on the blocking rate of application requests is ignored, so that the blocking rate is still high

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