Fog computing resource scheduling method and system

Abstract

The invention discloses a fog computing resource scheduling method and system. The method comprises the following steps: firstly, standardizing a fog computing resource set by using a translation-standard deviation transformation method; clustering the fog computing resource set subjected to standardization processing by utilizing a particle swarm optimization algorithm and a fuzzy clustering algorithm according to the fitness function, and determining an individual optimal position and a global optimal position of the fog computing resource set subjected to standardization processing; obtaining a globally optimal solution of the fog computing resource set according to the individual optimal position and the globally optimal position, and determining the category of the fog computing resource set; and according to the category of the fog computing resource set, performing resource scheduling according to the demand of a client for resources. According to the method, the fuzzy clustering algorithm and the particle swarm optimization algorithm are combined, and the fuzzy clustering optimization algorithm is used for solving the resource scheduling problem in fog computing, so that the resource scheduling is accurate.

Description

technical field [0001] The present invention relates to the field of information processing, in particular to a method and system for scheduling fog computing resources. Background technique [0002] Fog Computing: As a new computing paradigm, it extends the traditional cloud computing paradigm to the edge of the network. features such as geographical distribution. [0003] Fuzzy C-Means (FCM): As a data clustering algorithm, FCM determines the degree to which each sample point belongs to a certain cluster through the membership function. As a local optimization algorithm, the FCM algorithm seeks the optimal solution through the hill climbing method. [0004] Particle Swarm Optimization (PSO): The PSO algorithm is a heuristic algorithm proposed by Kennedy and Eberhart based on the foraging behavior of birds. It has the advantages of fast convergence and global optimization. [0005] In recent years, with the rapid development of the Internet of Things technology, a large...

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Problems solved by technology

[0007] However, the above method is aimed at the overall resources. When the number of resources is large, the resource search range is large, the processing time is long, and the clustering algorithm is sensitive to the initial value, and it is easy to fall into the problem of local optimal solution, making resource scheduling inaccurate.

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