Abstract
Emergency scheduling of public resources on the cloud computing platform network can effectively improve the network emergency rescue capability of the cloud computing platform. To schedule the network common resources, it is necessary to generate the initial population through the Hamming distance constraint and improve the objective function as the fitness function to complete the emergency scheduling of the network common resources. The traditional method, from the perspective of public resource fairness and priority mapping, uses incremental optimization algorithm to realize emergency scheduling of public resources, neglecting the improvement process of the objective function, which leads to unsatisfactory scheduling effect. An emergency scheduling method of cloud computing platform network public resources based on genetic algorithm is proposed. With emergency public resource scheduling time cost and transportation cost minimizing target, initial population by Hamming distance constraints, emergency scheduling model, and the corresponding objective function improvement as the fitness function, the genetic algorithm to individual selection and crossover and mutation probability were optimized and complete the public emergency resources scheduling. Experimental results show that the proposed method can effectively improve the efficiency of emergency resource scheduling, and the reliability of emergency scheduling is better.
Highlights
With the rapid development of the Internet, the number of network public resources is growing linearly
Is paper innovatively proposes a dynamic load prediction algorithm based on exponential smoothing, which has some improvements in the prediction accuracy and can be used to optimize the placement of virtual machines
Aiming at the problems of the above methods, an emergency scheduling method of cloud computing platform network public resources based on genetic algorithm is proposed. e Hamming distance constraint is used to generate the initial population of the genetic algorithm, and the selection operator, crossover operator, and mutation operator of the genetic algorithm are optimized. e experimental results show that the proposed method can obtain the global earliest completion time, and the reliability of emergency scheduling is better [15,16,17]
Summary
With the rapid development of the Internet, the number of network public resources is growing linearly. On the basis of the optimal solution, the method is obtained to complete the cloud computing platform network public emergency resources scheduling, and the specific process is as follows [13]. According to the above prediction results and the improved particle swarm optimization algorithm, the two objectives of public service quality and public resource utilization are optimized, and the emergency scheduling of public resources on cloud computing platform network is completed based on the optimal solution. In this way, through the above formula, other detailed information data of cloud storage information will be automatically collected inside the system.
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