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Emergency Scheduling Optimization Simulation of Cloud Computing Platform Network Public Resources

机译:云计算平台网络公共资源的紧急调度优化仿真

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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.
机译:云计算平台网络上公共资源的紧急调度可以有效地提高云计算平台的网络紧急救援能力。为了安排网络共同资源,有必要通过汉明距离约束生成初始群体,并将目标函数提高为完成网络共同资源的紧急调度的健身功能。传统方法从公共资源公平和优先级映射的角度来看,使用增量优化算法实现公共资源的紧急调度,忽略了目标函数的改进过程,这导致了不令人满意的调度效果。提出了一种基于遗传算法的云计算平台网络公共资源的紧急调度方法。通过紧急公共资源调度时间成本和运输成本最小化目标,汉明距离约束的初始群体,紧急调度模型和相应的客观函数改进作为适合函数,遗传算法针对各个选择和交叉和突变概率进行了优化和完整公共紧急资源调度。实验结果表明,该方法可以有效提高紧急资源调度的效率,急诊调度的可靠性更好。

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